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Glama

Mockdata

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

A4.4/5.0
Behavior5/5

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

Despite annotations already declaring readOnly, openWorld, idempotent, and non-destructive behavior, the description adds substantial beyond-schema transparency: multi-source cascading lookups, graceful degradation when GLEIF/OpenFIGI are unavailable, ambiguous matches returning figi_candidates rather than asserting, explicit unresolved identifiers, and source-labeling of resolved IDs. 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.

Conciseness2/5

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

Although the purpose and 'Use FIRST' guidance are front-loaded, the description becomes a dense, sprawling wall of text. The company type explanation is one giant unbroken parenthetical with embedded clauses, making it difficult to scan. Much of the detail is valuable, but it is not appropriately sized or well-structured for quick agent consumption.

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 complex tool with no output schema and only two parameters, the description is remarkably complete. It explains accepted input forms, resolution behavior, what happens on ambiguity, how unresolved IDs are surfaced, which sources back each identifier, and how failures degrade. An agent has enough context to invoke the tool correctly and interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the value parameter already includes examples and the 'entity name only' caution. The description adds meaning beyond the schema by explicitly listing ISIN as an accepted company input and clarifying how name matching behaves for bonds and non-equity instruments, improving an agent's chances of passing the right value.

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 verb and resource: 'resolve a user-spoken NAME to the canonical/official identifiers other tools require as input.' It also scopes the tool to two entity types and explicitly frames it as the first step when a name exists but an ID is needed, distinguishing it from profile/research siblings.

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 gives explicit when-to-use guidance: 'Use FIRST whenever you have a name but need an ID,' plus many example user phrasings that trigger this tool. However, it never names sibling alternatives or states when NOT to use it, so exclusions and direct comparison to tools like entity_profile or compare_entities are left implicit.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is overlap within the ask_pipeworx family (beta, grounded) and Polymarket tools (arbitrage, edges, fill_risk), which could cause confusion. Detailed descriptions mitigate this, but the boundaries are not always clear.

Naming Consistency3/5

Tool names use snake_case but lack a consistent verb_noun pattern. Some are imperative (discover_tools), others are descriptive (ask_pipeworx, bet_research), and some are noun phrases (entity_profile, recent_alerts). This inconsistency makes it harder to predict tool names.

Tool Count3/5

33 tools is on the higher side for a single server, but the broad scope (company data, prediction markets, memory, etc.) partially justifies the count. However, many tools are variations of core functionality, suggesting possible consolidation.

Completeness4/5

The tool set covers a wide range of data sources and tasks, including company profiles, comparisons, economic data, and prediction markets. The universal ask_pipeworx router fills most gaps, though some niche data sources might not be directly accessible.

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