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Glama

Altos

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?

Beyond the readOnly/openWorld/idempotent annotations, the description discloses substantial behavior: the internal cascade of multiple lookup endpoints, graceful degradation when GLEIF/OpenFIGI is unavailable, explicit listing of unresolved identifiers, and ambiguous matches returning figi_candidates. It also clarifies that non-equity instruments resolve and that ISINs are mapped to legal entities, which is far beyond annotation coverage.

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 lengthy but information-dense, with clear sectioning (SUPPORTED TYPES, behavior notes, degradation) and front-loaded usage examples. Every clause adds value, but the density and length prevent a top score for conciseness.

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?

With no output schema, the description carries the burden of explaining return behavior, and it does so thoroughly: identifiers are labelled with source, unresolved identifiers are explicitly stated, ambiguous company matches return figi_candidates, and enrichment failures still return EDGAR identifiers. It also explains the internal cascade, making the tool's behavior predictable for an agent.

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 crucial meaning: accepted formats per entity type, examples like 'ozempic'/'metformin', and a specific negative example explaining not to include trailing security-class words because the FIGI lookup matches instrument names. This compensates beyond what the schema provides, though not exhaustively.

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 uses a specific verb ('resolve') and resource ('user-spoken NAME to canonical/official identifiers') and provides concrete query patterns ('What's the ticker for…', 'find the CIK for…'). It clearly differentiates the tool's role from siblings by stating it produces identifiers that 'other tools require as input' and instructs to 'Use FIRST' when a name but no ID is available.

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 routing rule: 'Use FIRST whenever you have a name but need an ID.' It also explains what input forms are accepted and how enrichment degrades. However, it does not explicitly name sibling tools to avoid (e.g., entity_profile, compare_entities) or provide a when-not-to-use condition, so it stops short of a 5.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes; however, 'ask_pipeworx' and 'ask_pipeworx_grounded' are very similar, and 'polymarket_edges' vs 'polymarket_edge_tracker' could cause confusion. The real estate tools (altos_active_listings, altos_new_listings, altos_pending_sales) are clearly differentiated by status.

Naming Consistency4/5

Tool names use snake_case and are descriptive, but prefixes vary (altos_, polymarket_, ask_pipeworx, etc.) and patterns like 'entity_profile' or 'generate_llms_txt' don't follow strict verb_noun. Overall, naming is fairly consistent and readable.

Tool Count3/5

36 tools is high but justifiable given the broad scope (real estate, prediction markets, SEC/FDA data, etc.). Some utility tools (list_subscriptions, pipeworx_feedback) could be integrated, but the count is borderline heavy for a single server.

Completeness4/5

The server covers multiple domains thoroughly with tools for real estate, company profiles, prediction markets, and general data queries. Minor gaps exist (e.g., no dedicated tool for FDA drug details beyond ask_pipeworx), but meta-tools like deep_research fill many needs.

Resources