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Glama

Open Meteo

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

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, and the description adds substantial behavioral context beyond that: graceful degradation of LEI/FIGI enrichment, explicit unresolved identifiers, figi_candidates when ambiguous, and source labelling. It also clarifies internal cascading through multiple lookup endpoints, giving the agent accurate expectations for cost and latency.

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 packs a great deal of necessary information, but its structure is a single dense run-on block with heavy parentheticals and semicolons, making it harder to parse. The content earns its place, but the formatting harms scannability and the front-loaded examples are followed by an overwhelming wall of detail.

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?

For a tool with no output schema, the description fully compensates by covering return behavior: identifier labels, unresolved fields, figi_candidates, fallback behavior, and EDGAR's limitations. It also addresses edge cases like non-US issuers and non-equity instruments, so the agent has enough context to invoke and interpret this tool correctly.

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?

Schema has 100% parameter coverage, and the description still adds valuable semantics: it explains the exact input forms for value, warns never to pass the full noun phrase for bonds, gives an example of correct issuer formatting, and differentiates company vs drug inputs. This is far beyond what the schema alone conveys.

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?

Description states a specific action—'resolve a user-spoken NAME to the canonical/official identifiers'—and gives numerous concrete examples of user inputs and identifier outputs. It clearly distinguishes this tool from siblings like entity_profile or search_within by positioning it as the provider of the IDs that other tools require.

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?

Explicitly tells the agent to 'Use FIRST whenever you have a name but need an ID,' which is strong selection guidance. It also clarifies it replaces 2-3 manual lookups and lists supported entity types, but it does not explicitly name sibling tools to avoid or state when not to use it beyond not mentioning exclusions.

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

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as ask_pipeworx vs ask_pipeworx_grounded and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research). While some tools are clearly distinct (e.g., geocode vs forecast), the high number of similar tools increases the risk of agent misselection.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use a consistent verb_noun pattern (e.g., ask_pipeworx, resolve_entity), while others have no prefix (remember, recall) or use a domain prefix (polymarket_arbitrage, pipeworx_feedback). The mix of styles and lack of a unified pattern reduces predictability.

Tool Count2/5

With 33 tools, the set is too large for a focused server, especially given the name 'Open Meteo' which implies weather tools only. Many tools are redundant or cover vastly different domains, making the count feel bloated and difficult for an agent to navigate efficiently.

Completeness4/5

The tool set covers an impressively wide range of capabilities: weather data, SEC filings, Polymarket analysis, memory management, and more. For the actual scope of data querying and analysis, there are few obvious gaps (e.g., no direct database query tool beyond ask_pipeworx). However, the completeness relative to the implied weather domain is poor.

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