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Flickr Public

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. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description richly discloses behavior beyond the annotations: it cascades through multiple lookup endpoints, returns `figi_candidates` for ambiguous matches, lists unresolved identifiers explicitly, and handles LEI/FIGI gracefully degrading. It even explains ISIN-to-LEI mapping. No contradiction with readOnly/idempotent hints.

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

Conciseness4/5

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

The description is long but packed with essential behavioral and usage detail. It front-loads the core purpose and supports it with concrete examples. The length is justified given the tool's complexity, though a slight trim of repeated examples could make it tighter.

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

Completeness5/5

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

Given the tool's complexity, two required parameters, no output schema, and rich behavior, the description covers everything an agent needs to call it correctly: input semantics, edge cases, failure modes, and return candidates. Nothing critical is missing.

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

Parameters5/5

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

Although schema description coverage is 100%, the tool description adds crucial context not present in the schema, such as the exact format expected for `value`, the warning to pass only the issuer name for bonds, and the meaning of each identifier source. This significantly enhances the agent's ability to supply correct input.

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 a list of natural-language queries and a precise verb+resource pair ('resolve a user-spoken NAME to canonical/official identifiers'). It clearly distinguishes itself from sibling tools by stating what identifiers it produces and for which entity types, leaving no ambiguity about its purpose.

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?

Explicit guidance is given: 'Use FIRST whenever you have a name but need an ID.' It also details behavior for ambiguous matches and degradation, but it stops short of listing alternative tools to use when this one is not appropriate. Still, the primary spiel is clear enough.

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

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TDQS

B3.1/5.0
Disambiguation1/5

The tool set is extremely confusing because the server name 'Flickr Public' suggests a photo-sharing focus, but the vast majority of tools are for financial data, prediction markets, and other unrelated domains. Only 3 out of 33 tools are about Flickr, making it nearly impossible for an agent to understand the server's purpose or select appropriate tools.

Naming Consistency2/5

Tool names are all in snake_case, which is consistent, but there is no semantic pattern across the set. Verbs vary widely (ask, bet, by, compare, deep, discover, etc.) and many tools have descriptive but overly long names, mixing different styles. The lack of a common naming framework adds to the confusion.

Tool Count1/5

With 33 tools, the count is far too high for a server that should be about Flickr. The scope is massively overextended, covering multiple unrelated domains like SEC filings, Polymarket betting, and drug data. This mismatch makes the tool count feel bloated and inappropriate for the server's stated purpose.

Completeness1/5

For a Flickr server, the tool surface is severely incomplete. It only offers basic read operations (by_group, by_user, recent) and lacks core Flickr functionality such as uploading, editing, searching photos, or managing albums. The addition of hundreds of unrelated tools does not compensate for this gap, leaving the server's primary domain largely unaddressed.

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