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Glama

exchange

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

A4.6/5.0
Behavior5/5

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

Annotations already mark the tool read-only, open-world, and idempotent; the description adds substantial non-obvious behavior: identifiers are labelled by source, unresolved identifiers are explicitly listed under `unresolved`, ambiguous matches return `figi_candidates`, and LEI/FIGI enrichment degrades gracefully when upstream sources are unavailable. This goes well beyond what structured annotations or schemas provide.

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 front-loaded with examples and a clear 'Use FIRST' directive, and SUPPORTED TYPES provide useful structure. It is long and dense with parentheticals, but the complexity of the multi-source lookup behavior justifies most of the length. It could still be tightened without losing essential guidance.

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?

There is no output schema, so the description must convey return shape — and it does: CIK/ticker/company_name, LEI with parent/child ownership, FIGI candidates, unresolved identifiers, and RxCUI/ingredient/brand with a citation. It also covers input types, ambiguity handling, and failure modes, giving an agent enough to invoke and interpret 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%, and the schema's value description already includes the 'Pass the ENTITY NAME ONLY' warning. The description body adds meaningful semantics beyond the schema, such as accepting ISIN as a company input, exact-ticker mapping, and the bond-issuer matching caveat. Strong value over baseline, though some details overlap with the schema.

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?

States a specific verb ('resolve') and resource (user-spoken names → canonical official identifiers) and immediately distinguishes itself from downstream tools by noting these are IDs other tools require as input. Examples like 'ticker for...' and 'CIK for...' make the scope concrete. The title 'Resolve Entity' is expanded, not merely echoed.

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 says 'Use FIRST whenever you have a name but need an ID,' which is a strong when-to-use signal. It also clarifies edge cases (bonds resolve, ISINs, graceful degradation). However, it does not explicitly name sibling alternatives such as entity_profile or compare_entities for when not to use this tool, so it falls just short of full marks.

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
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions. Potential confusion exists between similar tools like ask_pipeworx and ask_pipeworx_grounded, but the descriptions differentiate them well. The high number of tools across multiple domains could still cause some ambiguity, but overall an agent can distinguish them.

Naming Consistency3/5

Naming patterns are mixed. Some subgroups follow consistent patterns (polymarket_*, pipeworx_*, get_*), but overall the set includes verb_noun (list_subscriptions, get_rate, remember) and noun_verb (entity_profile, deep_research) without a unified convention. The mixture of imperative and descriptive names reduces predictability.

Tool Count2/5

34 tools is excessive for a single server, bundling unrelated domains (currency, memory, data query, prediction markets, dependency scanning). This scope could be split into multiple servers for better coherence. The high count may overwhelm agents and increases the risk of misselection.

Completeness3/5

Each sub-domain (e.g., currency, memory, Polymarket) has reasonable coverage with common operations present. However, the server name 'exchange' is misleading—it suggests a narrower focus. Some gaps exist (e.g., no batch currency conversion, no direct Polymarket market detail tool). Overall, the set covers many tasks but lacks a unified purpose.

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