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Glama

Data Boston

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 this as read-only, idempotent, and non-destructive. The description goes well beyond that by disclosing cascading internal lookups, graceful degradation when GLEIF/OpenFIGI is unavailable, the `figi_candidates` behavior for ambiguous matches, explicit `unresolved` reporting, and the fact that it replaces multiple manual lookups.

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 and dense, but the complexity of the tool justifies most of the length. It is well-structured with clear labels, examples, and caveats, though a few sections could 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?

Given there is no output schema, the description does an excellent job of explaining expected results, edge cases, enrichment fallbacks, and identifier provenance. An agent has enough behavioral and contextual detail to invoke this tool correctly and interpret ambiguous or partial results.

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 schema coverage is 100%, the description adds substantial meaning beyond the schema: it explains input formats, accepted identifier types, exact-name-only guidance, the bond issuer caveat about trailing security-class words, and type-specific behaviors. This significantly helps agents construct valid `type` and `value` arguments.

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 precise purpose: resolving a user-spoken name to canonical/official identifiers that other tools require. It gives concrete query examples and names the supported entity types and identifier sources, making it clearly distinguishable from generic search or lookup tools.

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 explicitly says to use this tool FIRST whenever the user has a name but needs an ID, and provides examples of triggering queries. It does not explicitly name alternative sibling tools or state when not to use this tool, but the guidance is strong and context-rich.

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 occupy adjacent territory: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, ask_pipeworx_beta is currently an exact duplicate, and there are multiple polymarket-related tools. The descriptions are unusually explicit and cross-reference when to use which, which keeps this from scoring lower.

Naming Consistency4/5

All tool names use lowercase snake_case and group into recognizable families (boston_*, pipeworx_*, polymarket_*), giving the set a consistent feel. However, the set mixes verb_noun names, bare verbs like remember/forget, and adjective_noun phrases like recent_changes, so it is not a strict verb_noun pattern throughout.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold and feels bloated for a server with the narrow name "Data Boston"—only three tools directly concern Boston data, while the rest cover general research, prediction markets, subscription management, memory, AI visibility, and npm auditing. Most tools have a purpose, but the overall surface is not well-scoped.

Completeness4/5

For the broad data-access purpose, coverage is strong: routing, grounded verification, entity profiles, comparisons, change tracking, entity resolution, discovery, monitoring, memory, and Boston datasets are all represented. Minor gaps exist, such as no subscription update operation, no explicit fetch-by-citation tool, and limited boston_recent coverage, but agents can work around them.

Resources