Skip to main content
Glama

Hal Fr

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?

The description goes far beyond annotations by disclosing fallback behavior (LEI/FIGI enrichment degrades gracefully), how ambiguous matches are handled (returns `figi_candidates` instead of asserting), and that unresolved identifiers are explicitly listed under `unresolved`. It also notes internal cascading lookups, providing rich behavioral context not visible in 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 long but information-dense; every section earns its place given the tool's complexity. It is well-structured with clear type breakdowns and practical examples. Minor redundancy exists in the opening example list, but it does not detract from readability or utility.

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?

Without an output schema, the description compensates by explaining what the response includes (labelled identifiers, `unresolved` list, `figi_candidates`). It covers edge cases like non-US issuers, bonds, and drugs, and addresses graceful degradation. For a two-parameter tool with no output schema, this is exceptionally complete.

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 description coverage is 100%, the description adds critical semantic guidance: for `value`, it explains exactly what to pass for different entity types, including the warning to pass the issuer name only, not the full noun phrase. It also clarifies input formats like 'AAPL' vs '0000320193' and the ISIN mapping. This is genuine added value beyond 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?

The description clearly states the tool resolves user-spoken names to canonical identifiers, with specific verbs like 'resolve' and 'look up'. It differentiates from siblings by emphasizing it provides IDs for other tools and lists supported entity types (company, drug) with detailed identifier sources. The opening examples (ticker, CIK, LEI, RxCUI) make the purpose unmistakable.

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 'Use FIRST whenever you have a name but need an ID', giving clear guidance on when to use it. It does not explicitly name alternative tools or state when not to use it, but the boundary is implied by the context. The detailed handling of ambiguous matches and non-equity instruments adds practical usage context.

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

All Hal Fr toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.4/5.0
Disambiguation2/5

Several tools occupy nearly the same role: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are deliberately near-duplicates, while deep_research, validate_claim, bet_research, and the polymarket_* family all route factual questions to overlapping data pipelines. Generic single-word tools like get, search, structure, and author add further ambiguity, making it hard for an agent to confidently pick the right tool.

Naming Consistency3/5

Names are consistently lowercase snake_case, but they mix verb_noun tools (list_subscriptions, resolve_entity, validate_claim) with bare nouns (author, get, search, structure) and domain-prefixed families (ask_pipeworx, polymarket_*, pipeworx_*). The conventions are readable but not predictable enough to infer behavior from the name alone.

Tool Count2/5

35 tools is heavy for a single server, especially when many are meta-routers or near-variants of each other. The broad data-research scope justifies some breadth, but the surface feels padded with overlapping research and prediction-market tools rather than a tight, well-scoped set.

Completeness4/5

For its apparent purpose — authoritative data lookup, verification, research, entity profiling, prediction-market analysis, and monitoring — the surface is largely complete: retrieval, grounded answers, deep research, comparison, change tracking, subscriptions, and memory are all covered. Minor gaps exist, such as no direct tool for managing alert delivery or for some of the vague HAL-style operations, but agents can work around these via the router tools.

Resources