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Glama

Epss

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

A4.5/5.0
Behavior5/5

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

Annotations already set readOnly/idempotent, so the bar is lowered, and the description still adds valuable behavior: cascading internal lookups, graceful degradation when GLEIF/OpenFIGI fail, source-labelled identifiers, and explicit `unresolved` handling. It also states that ambiguous issuer names return `figi_candidates` rather than a single guess. 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?

The description is front-loaded with purpose and examples, but the company-type branch is an unwieldy run-on and contains a garbled, confusing segment ('it asserts nothing and returns figi_candidates...') that reads like injected text. It repeats schema-level input facts and would benefit from tighter bullet structure.

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 tool with no output schema, the description explains both return contents (CIK/ticker/company_name, LEI/ownership, FIGI, RxCUI/ingredient/brand) and failure behavior. It covers input formats, type limitations, and gracefully degraded output, so an agent can call and interpret results without extra lookups.

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?

Though schema covers both params, description materially deepens semantics: ticker/CIK/ISIN/name accepted for company, brand/generic for drug, and the crucial instruction to pass the entity name exactly as printed, never the question's noun phrase. This prevents common bond-lookup errors.

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 the exact task: resolve a user-spoken name to canonical/official identifiers, and enumerates supported types (company, drug) and ID outputs (CIK, LEI, FIGI, RxCUI). It distinguishes the tool as the identity-resolution step that feeds 'other tools require as input', separating it from sibling profile/research 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?

Explicit 'Use FIRST whenever you have a name but need an ID' gives a direct trigger condition. It also clarifies edge cases (bonds, ISINs, non-US issuers) but does not name sibling alternatives or when-not-to-use conditions.

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

A3.7/5.0
Disambiguation2/5

Several tools occupy overlapping functional space: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and bet_research, polymarket_edges, and polymarket_arbitrage all target Polymarket opportunity detection. The detailed descriptions help, but an agent can easily select the wrong query/research or prediction-market tool.

Naming Consistency3/5

Most names are readable snake_case and clusters like polymarket_* and pipeworx_* are internally consistent. However, the overall set mixes verb_object names (compare_entities, resolve_entity), bare verbs (forget, subscribe), and noun phrases (entity_profile, recent_alerts, top_exploited), so there is no unifying naming convention.

Tool Count2/5

At 33 tools, the count exceeds the reasonable threshold for a focused server, and the problem is worse because the server is named Epss while most tools are Pipeworx data, Polymarket, memory, and subscription tools. A focused EPSS server would need only a handful of tools; this is a grab bag.

Completeness2/5

For the EPSS purpose implied by the server name, only get_epss and top_exploited exist, with no CVE search, historical score context, or vulnerability-management tooling. The unrelated research, memory, and prediction-market tools are individually fairly complete, but they do not fill the gap for the apparent EPSS use case.

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