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Glama

Security Feeds

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 declare readOnly, idempotent, and openWorld behavior. The description adds rich operational detail: it cascades through several lookup endpoints, degrades gracefully when LEI/FIGI enrichment fails, returns figi_candidates on ambiguous matches, and explicitly reports unresolved identifiers. This goes far beyond the annotations and gives the agent realistic expectations.

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 densely informative, with examples front-loaded and the primary use cue near the beginning. Length is justified by the tool's two entity types and multi-source identifier behavior, though some parentheticals could be trimmed without loss.

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 the tool's complexity and the absence of an output schema, the description is remarkably complete. It covers input forms, return behavior, ambiguity handling, unresolved identifiers, source attribution, graceful degradation, and the ISIN-to-LEI edge case. An agent has enough context to select and invoke this tool correctly.

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?

Schema description coverage is 100%, but the description adds substantial value: concrete examples for both types, the 'entity name only' rule, and a critical bond-specific warning about trailing security-class words. This meaningfully improves the likelihood of correct invocation beyond the schema alone.

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 specific verb and resource: resolve a user-spoken NAME to canonical identifiers. It lists supported types with concrete examples and clarifies its role as the provider of IDs that other tools require, distinguishing it from sibling tools like entity_profile and deep_research.

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,' providing a clear trigger condition. It also covers input-format pitfalls, such as not passing full noun phrases for bonds, but it does not explicitly name alternatives or state when not to use the tool.

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

A3.9/5.0
Disambiguation2/5

The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded trio creates real selection ambiguity — beta is currently identical to the stable router, and grounded shares the same routing with an added evidence step. Polymarket tools also overlap (polymarket_edges vs polymarket_arbitrage both surface structural arbitrage), and entity_profile/recent_changes both pull filings and news.

Naming Consistency4/5

Most tools follow a snake_case verb_noun pattern (resolve_entity, list_feeds, validate_claim, compare_entities). Minor deviations exist — bare verbs (remember, recall, forget, subscribe, unsubscribe) and noun-first names (entity_profile, recent_changes, pipeworx_trending) — but the overall convention is consistent and readable.

Tool Count2/5

34 tools is excessive for a server nominally named 'Security Feeds' — only three tools actually relate to security feeds. Even as a broad data-research platform, the surface feels bloated with five near-exclusive Polymarket tools, three memory tools, and four subscription-lifecycle tools that could be consolidated.

Completeness4/5

For the actual domain revealed by the tools (data research, entity intelligence, prediction markets, subscriptions, feeds), coverage is strong: resolution, profiles, changes, comparison, claim verification, memory CRUD, subscription lifecycle, and feed operations are all present. Minor gaps include no subscription-update tool and no direct feed-search tool, but the ask_pipeworx router compensates.

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