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Glama

Openagenda

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 convey read-only, idempotent, non-destructive behavior, so the bar for extra value is high. The description exceeds it by disclosing ambiguity handling ('asserts nothing and returns figi_candidates'), explicit 'unresolved' reporting, graceful degradation when GLEIF/OpenFIGI are unavailable, and internal cascading lookups. 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.

Conciseness4/5

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

The description is long, but the length is largely justified by the tool's multi-source complexity and edge cases. It front-loads the use case and supports types, then layers detail in a structured way. Slight verbosity in the parenthetical explanations keeps it from a perfect conciseness score.

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 carries the burden of explaining return behavior, and it does: identifiers are labelled with source, unresolved identifiers are listed, ambiguous matches return figi_candidates, and enrichment failures are handled gracefully. Given the tool's complexity, this is a complete and usable contract for an agent.

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 coverage is 100%, the description substantially enriches both parameters. For 'value' it provides accepted forms (ticker, CIK, ISIN, brand/generic name), a critical format rule ('Pass the ENTITY NAME ONLY'), and a concrete negative example for bond issuer lookups. For 'type' it details what each enum value resolves and which sources back it.

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 opens with a set of concrete user phrasings and then states the core function in a specific verb+resource form: resolve a user-spoken NAME to the canonical/official identifiers other tools require. It also differentiates from siblings by listing supported entity types and emphasizing this is an ID-resolution step, not profiling or validation.

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 an explicit when-to-use rule: 'Use FIRST whenever you have a name but need an ID.' It also explains what the tool replaces and its scope. It does not explicitly name alternatives or state when not to use it, so it stops short of the strongest usage-guideline pattern.

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
Disambiguation3/5

Several tools have overlapping roles: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and suggest_questions all answer or route questions, and ask_pipeworx_beta is currently identical to ask_pipeworx. The descriptions do a good job of prescribing when to use each, and the polymarket, memory, and subscription families are mostly distinct, but boundaries are easy to miss.

Naming Consistency3/5

Most names are readable snake_case and families like ask_pipeworx_* and polymarket_* are consistent, but the set mixes verb_noun tools like compare_entities and validate_claim with noun phrases like entity_profile and recent_changes, plus bare verbs like events and forget. There is no single predictable naming pattern across the full set.

Tool Count2/5

33 tools is well over the 25+ threshold, and the count is padded by a large Pipeworx research and prediction-market stack alongside just two OpenAgenda-specific tools. A server named Openagenda would be better served by a much smaller, focused event-calendar set, or by splitting unrelated capabilities into separate servers.

Completeness2/5

For OpenAgenda, the surface is essentially read-only: search_agendas plus events, with no agenda/event creation, update, deletion, or detailed single-resource views. The Pipeworx side is comparatively richer, but that does not fill the gaps in the domain the server name advertises.

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