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Glama

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

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Even with readOnly/idempotent annotations, the description adds substantial behavioral context: graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` reporting, ambiguous-name handling via `figi_candidates`, and cross-source identifier provenance. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but deliberately structured: trigger phrases first, then type-specific resolution behavior, then degradation/performance context. Every sentence adds operational detail an agent needs; no filler or redundant restatement of the schema.

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 covers what will be returned for each type, what happens on ambiguity, and what happens when enrichment sources fail. The agent has enough information to decide when to call it and what to expect in the result.

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?

The schema already documents both parameters at high coverage, and the description goes well beyond it: it gives valid input forms for each type, concrete examples, and a critical bond-name caveat ('pass the ENTITY NAME ONLY... trailing security-class words match nothing'). This materially improves correct invocation.

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 names a specific operation — resolving a user-spoken name to canonical/official identifiers — and lists concrete trigger phrases plus supported entity types. It clearly positions itself as the name-to-ID entry point that feeds other tools, distinguishing its role from sibling lookup/entity 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?

It explicitly instructs 'Use FIRST whenever you have a name but need an ID' and gives trigger examples. It does not name specific sibling alternatives or state when not to use it, so it stops just short of a full exclusion/alternative matrix.

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

A3.8/5.0
Disambiguation2/5

Several tool groups have heavy functional overlap: ask_pipeworx, ask_pipeworx_beta (explicitly identical to stable right now), ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all route around the same data-querying core. The Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) also blurs together, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Most tools follow a clear lowercase snake_case convention with family prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*. Minor deviations exist — bare verbs like remember/recall/forget and the quirky generate_llms_txt — but the overall pattern is predictable and readable.

Tool Count2/5

33 tools is already on the heavy side, but the real problem is scope: the server is named 'Emoji' yet only 2 of 33 tools relate to emoji, with the other 31 forming a sprawling data-research/prediction-market platform. The count feels mismatched with the server's apparent identity.

Completeness2/5

The data-research side is impressively broad, but the stated domain (Emoji) is barely covered — only lookup and keyword search with no listing, metadata, or classification features. The toolset is a grab bag of unrelated domains, so the overall surface is not complete for any single coherent purpose.