Skip to main content
Glama

Tinder Bio

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  2. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses extensive behavioral traits: enrichment from GLEIF and OpenFIGI, graceful degradation of LEI/FIGI when providers are unavailable, internal cascading through multiple endpoints, resolution of non-equity instruments, handling of ambiguous matches via figi_candidates, and explicit reporting of unresolved identifiers. It also details the drug type's output (RxCUI, ingredient, brand, citation). This fully transparent description adds significant value over the annotations and contains no contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, dense paragraph that front-loads purpose and usage but is notably long (roughly 250 words). While every piece of information is relevant and the structure uses lists and semicolons effectively, it lacks the brevity that would make it easier to parse quickly. It could be split into more digestible sections without losing content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (two entity types, multiple data sources, enrichment, ambiguity handling, graceful degradation) and the lack of an output schema, the description is remarkably complete. It explains the full set of returned identifiers (CIK, ticker, company_name, LEI, FIGI), the figi_candidates array for ambiguous matches, and the unresolved array. It also specifies input formats and edge cases. The only minor gap is the absence of an explicit JSON return structure, but the description's depth compensates.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema describes type and value generically, but the description enriches them substantially. For 'value', it explains how to handle ticker, CIK, ISIN, names, and the critical caveat about trailing security-class words for bond issuers. For 'type', it details the differences between company and drug, including the specific identifiers returned for each. This goes well beyond the schema's 100% coverage and helps the agent format inputs correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with concrete example queries and explicitly states it resolves a user-spoken name to canonical identifiers. It lists the specific identifier types (CIK, ticker, LEI, FIGI, RxCUI) and differentiates itself by instructing 'Use FIRST whenever you have a name but need an ID.' This clearly defines its role and distinguishes it from any generic lookup tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives an explicit when-to-use directive ('Use FIRST whenever you have a name but need an ID') and provides detailed input formatting rules (e.g., pass only the entity name, not the full noun phrase, for bonds). While it doesn't explicitly name alternative tools or when-not-to-use scenarios, the precedence instruction and the comprehensive parameter guidance strongly inform correct usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Tinder Bio toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially within separate domains. However, the large number of Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) may cause confusion as they overlap in functionality, though descriptions help differentiate them.

Naming Consistency3/5

All tool names use snake_case, but the naming patterns are inconsistent: some are bare verbs (forget, recall), some are verb_noun (generate_llms_txt), and others are noun_compound (polymarket_arbitrage). This mixed convention reduces predictability.

Tool Count3/5

With 31 tools, the set is on the higher end. While many tools serve distinct data-querying and prediction-market needs, the count feels heavy for the server's stated purpose (Tinder Bio), and some tools (e.g., pipeworx_trending, pipeworx_feedback) could be considered bloat.

Completeness1/5

The server name implies a focus on dating profile bio generation, yet only one tool (tinder_bio_generate) addresses this. The vast majority of tools are unrelated (e.g., SEC filings, Polymarket bets), creating a severe mismatch between the server's title and its actual functionality.

Resources