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Data Montgomery

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 signal read-only, idempotent, and non-destructive behavior, but the description adds substantial non-obvious behavior: it cascades through multiple lookup endpoints, degrades gracefully when GLEIF/OpenFIGI is unavailable, returns figi_candidates instead of asserting on ambiguous matches, lists unresolved identifiers explicitly, and maps ISINs to legal entities. This is exactly the kind of behavioral context annotations cannot 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 long but front-loaded with trigger phrases and uses capital labels like SUPPORTED TYPES and entity-type headers. Every sentence earns its place; however, the company type section is one dense paragraph that could be broken into bullets for easier scanning by an agent.

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 carry return-value semantics. It does: it lists the identifiers returned for each type, explains ambiguous matches via figi_candidates, explicitly mentions the unresolved field, and notes graceful degradation. For a complex resolver with multiple backends, this is complete enough for correct selection and invocation.

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?

Even though the schema already documents both parameters, the description adds crucial semantics beyond it: pass the entity name only, for a bond use the issuer exactly as printed, never include the full noun phrase from the question, and accept ticker/CIK/ISIN/name for companies or brand/generic name for drugs. It also gives concrete examples like AAPL, 0000320193, 'ozempic', and 'NEW YORK ST DORM AUTH'.

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 trigger phrases and states the exact job: resolving a user-spoken name to canonical/official identifiers (CIK, ticker, LEI, FIGI, RxCUI). It also frames these as 'identifiers other tools require as input,' which separates it from downstream tools that consume IDs, giving clear scope and identity.

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 provides explicit timing and context: 'Use FIRST whenever you have a name but need an ID,' plus a list of natural-language triggers and supported entity types. It does not explicitly name alternative sibling tools or state when not to use this tool, so it stops short of full exclusion guidance.

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

A4.1/5.0
Disambiguation3/5

Several tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, and deep_research, which may confuse an agent about which to use for a given query. Additionally, the many prediction market tools (polymarket_arbitrage, polymarket_edges, etc.) have subtle distinctions that could lead to misselection.

Naming Consistency4/5

Tool names are predominantly lowercase with underscores and follow a descriptive pattern (e.g., compare_entities, resolve_entity, scan_dependency). There are minor deviations like bet_research vs. research-related tools, but the overall pattern is consistent.

Tool Count3/5

With 33 tools, the server covers many domains (data lookup, prediction markets, Montgomery County data, npm scanning, etc.), making it feel heavy. While each tool has a clear purpose, the broad scope borders on excessive for a single server.

Completeness3/5

The server provides a wide range of data access and analysis tools, but it lacks basic CRUD operations for its data sources (e.g., no way to create or update records in Montgomery County data or Pipeworx). Some domain coverage is incomplete (e.g., no tool for listing all Pipeworx tools, only discover_tools with top-N results).

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