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Glama

Geodistance

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?

Annotations already mark it read-only and idempotent; the description adds genuine behavioral context: graceful degradation of LEI/FIGI enrichment, internal cascading across lookup endpoints, explicit unresolved handling, and coverage of non-US issuers. None of this contradicts annotations.

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 the core directive and supported types, and most sentences carry operational value. It is somewhat sprawling due to long parentheticals and embedded clauses, so a more structured layout would earn full marks, but it is not padded.

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 two-parameter tool with no output schema, the description covers accepted inputs, per-type returned identifiers, failure/degradation behavior, and output conventions such as unresolved being explicitly listed. An agent has enough to select and invoke the 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?

Although schema coverage is 100%, the description significantly extends parameter meaning: it gives concrete ticker/CIK/name examples, explains the ISIN-to-LEI path, and warns to pass only the entity name, never the full noun phrase, with a bond-specific example. This goes well beyond the baseline schema descriptions.

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 states a specific action: resolving a user-spoken NAME to canonical identifiers needed by other tools, with examples of natural-language queries. It clearly names the resource kinds (company, drug) and distinguishes this tool from siblings like entity_profile by framing it as the ID-resolution entry point.

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?

It explicitly says 'Use FIRST whenever you have a name but need an ID' and explains which entity types resolve and how ticker/CIK/ISIN/name inputs map to outputs. It does not explicitly name sibling alternatives or state when not to use the tool beyond an input-format caveat, so it stops short of full when-not/alternative guidance.

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

All Geodistance toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Many tools have clearly distinct purposes, but research entry points overlap heavily (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the beta tool currently behaves identically to the stable version. Polymarket tools and AI visibility tools also create multiple near-overlapping options that require careful reading to disambiguate.

Naming Consistency3/5

All names follow snake_case and are readable, but there is no consistent pattern: some are verb-first (ask_, resolve_, validate_, scan_), some noun-first (entity_profile, pipeworx_trending, polymarket_edges), and some are bare nouns (distance, destination). This mixed convention makes the tool set feel less predictable than it should.

Tool Count2/5

35 tools is excessive for a server named Geodistance, and the actual purpose is a sprawling data-research and prediction-market toolkit with a few geospatial helpers. The set would be better split into focused servers; as-is it feels bloated and poorly scoped.

Completeness3/5

The data-research surface is quite complete (discovery, lookup, grounded answers, comparisons, subscriptions, memory, feedback), and geodistance has core operations like distance, destination, and coordinate conversion. However, the geographic functionality is thin and the overall grab-bag composition makes it hard to assess completeness against any single coherent domain; obvious geospatial features like geocoding, routing, or area calculations are absent.

Resources