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Glama

Imf

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

A4.5/5.0
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 substantial behavior: it cascades through multiple lookup endpoints, degrades gracefully when GLEIF/OpenFIGI is unavailable, returns explicit `unresolved` fields, and asserts nothing when a name matches multiple instruments. It also explains how ISINs resolve to legal entities via GLEIF, adding value the annotations cannot convey. No contradiction with annotations exists.

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 front-loaded with examples and the core purpose, and nearly every segment adds useful information. However, the 'company' type is one extremely long, parenthetical-heavy run-on sentence that is hard to scan; bulleted sub-structure would greatly improve readability. It is information-dense but not concise.

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 explains the key return behaviors: `figi_candidates` for ambiguous matches, explicit `unresolved` identifiers, source-labelled identifiers, and RxCUI/brand/ingredient returns for drugs. It also covers edge cases like non-US issuers and non-equity instruments, making the description sufficiently complete for an agent to select and call the tool correctly.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaningful constraints beyond the schema: for bonds it tells the caller to pass 'the ISSUER exactly as printed' and never trailing security-class words. It maps each entity type to accepted input forms (ticker, CIK, ISIN, brand/generic name), which helps agents populate the `value` parameter 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 user phrasings and states a specific action: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It clearly distinguishes this from siblings like entity_profile and compare_entities by focusing on identifier lookup rather than profile or comparison. The supported types and identifier outputs are enumerated, leaving no ambiguity about what the tool does.

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 explicit usage guidance: 'Use FIRST whenever you have a name but need an ID.' It also explains when ambiguity is expected (multiple FIGI candidates) and when identifiers may be unresolved. However, it does not name specific sibling tools or state when NOT to use this tool versus alternatives such as entity_profile, so some routing knowledge is left implicit.

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

A3.9/5.0
Disambiguation2/5

Several natural-language query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) have heavily overlapping purposes, and ask_pipeworx_beta currently behaves identically to ask_pipeworx. The five Polymarket tools also have subtle boundaries, though the IMF, memory, and subscription clusters are clearly separated.

Naming Consistency4/5

Most tools follow a clean snake_case verb_noun pattern (get_data, resolve_entity, subscribe, compare_entities). Minor deviations exist: noun-first names like entity_profile and ai_visibility_check, brand-prefixed names like pipeworx_feedback and pipeworx_trending, and ask_pipeworx_beta using a suffix instead of an underscore.

Tool Count2/5

34 tools is well above the range that remains easily navigable, and the count is inflated by many meta-tools, overlapping query entry points, and five distinct Polymarket tools. The server is named Imf, yet it also carries npm dependency scanning, llms.txt generation, AI visibility checks, and prediction-market tooling, making the scope feel unfocused.

Completeness3/5

Subdomain lifecycles are reasonably covered: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and data access has discovery, lookup, grounding, and research paths. However, the overall domain is so broad that a complete surface is hard to define, and the IMF-specific portion is thin (only get_data, get_datasets, and search_indicators).

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