The MCP tool directory.

Generate one production-ready LinkedIn post.

Fetch current credit usage and billing period boundaries.

Generate 5-10 punchy LinkedIn topic ideas from a raw thought.

List editable custom voices only (tone presets excluded).

List paginated standalone lead magnets with preview/download fields.

List paginated post drafts.

List supported post type keys and labels.

List static template catalog with optional post-type filter.

Rewrite an existing post based on user instruction (no credit consumption).

Extract forensic Voice DNA and mapped suggestions from historical posts.

Update a post draft by id.

Update a custom voice by id.

Search law-ordinance links by ministry (연계 법령 소관부처별 목록 조회). This tool searches local ordinances linked to laws managed by a specific government ministry or department. Args: org: Ministry/department code (required, e.g., "1400000") display: Number of results per page (max 100, default 20). **Recommend 50-100 for law searches (법령 검색) to ensure exact matches are found.** page: Page number (1-based, default 1) oc: Optional OC override (defaults to env var) type: Response format - "JSON" (default), "XML", or "HTML" sort: Sort order ctx: MCP context (injected automatically) Returns: Search results with ministry's linked ordinances or error Examples: Search ordinances linked to ministry 1400000: >>> lnkDep_search(org="1400000", type="XML")

Search laws linked to local ordinances (법령-자치법규 연계 목록 조회). This tool searches Korean laws that have linkages to local ordinances. Useful for understanding how national laws relate to local regulations. Args: query: Search keyword (default "*") display: Number of results per page (max 100, default 20). **Recommend 50-100 for law searches (법령 검색) to ensure exact matches are found.** page: Page number (1-based, default 1) oc: Optional OC override (defaults to env var) type: Response format - "JSON" (default), "XML", or "HTML" sort: Sort order - "lasc"|"ldes"|"dasc"|"ddes"|"nasc"|"ndes" ctx: MCP context (injected automatically) Returns: Search results with linked laws list or error Examples: Search for "자동차관리법": >>> lnkLs_search(query="자동차관리법", type="XML")

Search ordinance articles linked to law articles (연계 법령별 조례 조문 목록 조회). This tool searches local ordinance articles that are linked to specific national law articles. Shows which local ordinances implement or relate to specific law provisions. Args: query: Search keyword (default "*") display: Number of results per page (max 100, default 20). **Recommend 50-100 for law searches (법령 검색) to ensure exact matches are found.** page: Page number (1-based, default 1) oc: Optional OC override (defaults to env var) type: Response format - "JSON" (default), "XML", or "HTML" knd: Law type code (to filter by specific law) jo: Article number (4 digits, zero-padded). Examples: "0002" (Article 2), "0020" (Article 20), "0100" (Article 100) jobr: Branch article suffix (2 digits, zero-padded). Examples: "00" (main article), "02" (Article X-2) sort: Sort order ctx: MCP context (injected automatically) Returns: Search results with linked ordinance articles or error Examples: Search ordinances linked to 건축법 시행령: >>> lnkLsOrdJo_search(knd="002118", type="XML") Search specific article (제20조): >>> lnkLsOrdJo_search(knd="002118", jo=20, type="XML")

Authenticate MCP session to a Signal Found client account. Run this at the start of each session before business tools. Most tools require authenticated context and will use this session client id unless you pass an explicit `client_id` argument.

Clear the active authenticated client context for this MCP server session.

Get data about players based on first, last, or full name.

Search for token addresses by symbol or name. Returns multiple potential matches based on symbol or token name similarity. Only the first ``TOKEN_RESULTS_LIMIT`` matches from the Blockscout API are returned.

Contemplative reasoning tool. Use for complex problems needing multi-perspective understanding, contradictions requiring integration, or questions holding their own wisdom. **Workflow:** Always start with tag='begin' (returns framework). Then continue with contemplation tags. Do NOT output wisdom until status='WISDOM_READY'. **Tags:** begin (FIRST - receives framework), then: open/engage/express (process), examine/reflect/verify/refine/complete (meta-cognitive), recognize/transform/integrate/transcend/embody (non-dual), upaya/expedient/direct/gradual/sudden (skillful-means), meditate (pause).

Get a summary of the current contemplative journey

Search antibody-antigen relations. IMPORTANT: You must provide `target_name` as a search parameter. Use this tool when the user wants to find antibodies associated with a target antigen and optionally narrow the result set with facet filters. The Python service only validates the request shape and forwards the payload to the downstream Java MCP resource. Args: target_name: str, antigen target name used for exact or keyword lookup offset: int, pagination offset, starting from 0 limit: int, page size for downstream query filter: object, optional facet filters. Supported fields: - target_name: List[str], filter by target names - source: List[str], filter by data sources - antigen_species: List[str], filter by antigen species - antibody_name: List[str], filter by antibody names Returns: Dict[str, Any]: Response body: { "total": 14855, "items": [ { "source": "patent", "patent": { "PATENT_ID": "8ccf84a5-3a5a-4ac4-b2a5-4bcf5fc4960a", "PN": "US20190185568A1", "TITLE": "Anti-PD-1 antibody", "ABST": "..." }, "hchain_sequence_text": "...", "lchain_sequence_text": "...", "target_name": "PD-1", "related_gene_symbol": "PD-1", "target_species": "Bos taurus", "target_id": "9786d32e3d244a688ec6a60d2fe1b10b" } ], "facets": [ { "field": "source", "values": [{"name": "patent", "count": 14772}] } ] } - total: total hit count. - items: matched antibody-antigen records. - facets: filter buckets in {"field", "values"} format.

Submit a modification search job. Use this tool when the user wants to search bio sequence records by modification conditions and optionally narrow the result set with sequence length or a query sequence. The Python service validates the request shape and forwards the payload to the downstream Java MCP resource. Tool flow: 1. Call this tool `ls_modification_search_submit` to submit the job and get `job_id`. 2. Call `ls_sequence_search_check_status` with `job_id` to poll backend status. 3. After the status becomes success, call `ls_sequence_search_get_results` with `job_id` to fetch paged results. Args: sequence_type: str, query sequence type, supports "NUCLEOTIDE" and "PROTEIN" sequence: str, optional query sequence string used for alignment narrowing database: List[str], target database list, supports "ALLPATENT" and "CLAIMS" subject_length: object, optional subject length filter, format {"start": int, "end": int} modification_location: List[object], required modification filter list. Each item supports: - location: List[str], required modification positions or ranges - modification_name: List[str], required modification names or keywords - operation: str, optional logical operation, supports "AND", "OR", "NOT" - match_type: str, optional match type, supports "any_contain", "any_match", "all_contain", "all_match" limit: int, maximum returned result count for the backend task Returns: Dict[str, Any]: Response body: { "job_id": "2c7eec32368fa5d6787be51aaaa214a4" } - job_id: backend search job ID for status/result polling.

Fetch patent-related sequence details. Fetch sequences associated with a patent by patent ID or patent number. Args: patent_id: str, patent ID used to fetch related sequences. pn: str, patent number used to resolve patent ID before fetching sequences. offset: int, pagination offset, starting from 0. limit: int, page size. Must be between 1 and 100. Returns: Dict[str, Any]: Response body: { "total": 85, "items": [ { "sequence_number": 661, "length": 106, "sequence_type": "PROTEIN", "sequence": "TVAAPSVFIFPPSDEQ..." } ] } - total: total hit count for the patent sequence query. - items: matched sequence records in current page.

Related law search (관련법령 조회). This tool searches for laws related to a given query term. Part of the 법령정보 지식베이스, it identifies associations between laws based on shared subject matter or cross-references. Args: query: Search keyword (default "*") display: Number of results per page (max 100, default 20) page: Page number (1-based, default 1) oc: Optional OC override (defaults to env var) type: Response format - "JSON" (default), "XML", or "HTML" ctx: MCP context (injected automatically) Returns: Search results or error

Run sequence alignment. Use this tool when the user wants to align biological sequences directly. The Python service validates the request shape and forwards the payload to the downstream Java MCP resource. Args: sequence_type: str, sequence type, supports "NUCLEOTIDE" and "PROTEIN" alignment_type: str, alignment type, supports "PSA" for pairwise sequence alignment and "MSA" for multiple sequence alignment sequences: List[str], target sequences for PSA, or all sequences to align for MSA. MSA requires at least 2 sequences. query_sequence: str, required for PSA pairwise sequence alignment Returns: Dict[str, Any]: Response body: { "msa_hits": [ { "hit_start": 1, "hit_end": 12, "alignment_text": "MSTNPKPQRKTK", "sequence_name": "Seq_1" } ], "psa_hits": [ { "query_start": 1, "query_end": 12, "target_start": 1, "target_end": 12, "alignment_text": "..." } ] } - msa_hits: multiple sequence alignment rows for MSA requests. - psa_hits: pairwise alignment rows for PSA requests. - the response contains one of the two fields depending on alignment_type.

Fetch sequence details in batch. Batch fetch full detail records by sequence number. Args: sequence_numbers: List[int]. Sequence number list used to fetch sequence details. Each item must be an integer. Returns: Dict[str, Any]: Response body: { "items": [ { "sequence_number": 1, "length": 332, "sequence_type": "PROTEIN", "organism": ["Bombus terrestris", "unidentified"], "is_antibody": false, "drugs": [], "genes": [], "sequence_name": "Silk protein BBF3 (Bombus terrestris precursor)", "sequence": "MQIPAIFVTCLL..." } ] } - items: matched sequence detail records for the requested sequence_numbers.

Get the current execution status. Use this tool after `ls_sequence_search_submit` or `ls_modification_search_submit` to check the current backend status of a submitted job. Args: job_id: str, backend search job ID Returns: Dict[str, Any]: Response body: { "status": "SUCCESS" } - status: backend job status string, commonly SUCCESS, RUNNING, or FAILED.

Get paged results. Use this tool after `ls_sequence_search_check_status` reports success for a job created by `ls_sequence_search_submit` or `ls_modification_search_submit`. It retrieves the result page directly returned by the downstream Java MCP resource. Args: job_id: str, backend search job ID offset: int, result offset starting from 0 limit: int, page size for the downstream query sort_field: str, optional sorting field order: str, optional sorting order, supports "DESC" and "ASC" perc_identity: object, optional identity filter, format {"start": number, "end": number} q_perc_identity: object, optional query identity filter, format {"start": number, "end": number} s_perc_identity: object, optional subject identity filter, format {"start": number, "end": number} qcov_hsp_perc: object, optional query coverage filter, format {"start": number, "end": number} scov_hsp_perc: object, optional subject coverage filter, format {"start": number, "end": number} Returns: Dict[str, Any]: Response body: { "total": 5, "items": [ { "sequence_id": "16379347", "sequence_type": "PROTEIN", "sequence_len": 12, "score": 48, "q_covs": 100.0, "s_covs": 100.0, "q_coverage": "12/12", "s_coverage": "12/12", "evalue": "8.11983e-07", "identity": "12/12", "identity_value": 1.0, "q_identity": "12/12", "q_identity_value": 1.0, "s_identity": "12/12", "s_identity_value": 1.0, "positive": "12/12", "positive_value": 1.0, "modification": "No", "claimed": "Yes", "substance_name": ["..."], "hit": { "gaps": "0/12", "q_start": 1, "q_end": 12, "q_string": "MSTNPKPQRKTK", "s_start": 1, "s_end": 12, "s_string": "MSTNPKPQRKTK", "matched": "MSTNPKPQRKTK" } } ], "facets": [ { "field": "perc_identity", "statics": {"100": 5}, "range_value": {"max": 100, "min": 100} } ] } - total: total hit count. - items: current page of match records (empty list if no hit). - facets: optional facet/statistics buckets (can be empty list).

Submit a sequence search job. Use this tool when the user wants to launch a bio sequence search job. The Python service only validates the request shape and forwards the payload to the downstream Java MCP resource. Tool flow: 1. Call this tool `ls_sequence_search_submit` to submit the job and get `job_id`. 2. Call `ls_sequence_search_check_status` with `job_id` to poll backend status. 3. After the status becomes success, call `ls_sequence_search_get_results` with `job_id` to fetch paged results. Args: sequence_type: str, query sequence type, supports "NUCLEOTIDE" and "PROTEIN" query_type: str, target sequence type, supports "NUCLEOTIDE" and "PROTEIN" sequence: str, single query sequence string database: List[str], target database list, supports "ALLPATENT" and "CLAIMS" strand: List[str], optional nucleotide alignment direction filter, supports "PLUS" and "MINUS" has_modification: List[bool], optional chemical modification presence filter, supports true and false evalue: float, expect threshold with_gaps: bool, whether sequence alignment includes gaps perc_identity: object, optional identity filter, format {"start": int, "end": int} qcov_hsp_perc: object, optional query coverage filter, format {"start": int, "end": int} scov_hsp_perc: object, optional subject coverage filter, format {"start": int, "end": int} q_perc_identity: object, optional query identity filter, format {"start": int, "end": int} s_perc_identity: object, optional subject identity filter, format {"start": int, "end": int} subject_length: object, optional subject length filter, format {"start": int, "end": int} limit: int, maximum returned result count for the backend task Returns: Dict[str, Any]: Response body: { "job_id": "9415e8160da128759739c60a18748415" } - job_id: backend search job ID for status/result polling.