Skip to main content
Glama

zenquotes

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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Even though annotations already declare the tool read-only and idempotent, the description goes well beyond them by disclosing cross-source resolution behavior, the 'asserts nothing and returns figi_candidates' ambiguity policy, explicit unresolved identifier reporting, and graceful degradation when GLEIF or OpenFIGI fail. These are non-obvious and highly valuable for correct invocation.

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 dense and front-loaded, with trigger examples first and type-specific detail following, and every clause carries real semantic weight. However, the company branch is a long run-on with multiple nested parentheticals, so a little restructuring or segmentation would make it even easier to scan.

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 notably complete: it covers input variants, source provenance, candidate-selection behavior, unresolved-identifier semantics, fallback behavior, and return concepts like figi_candidates and unresolved. An agent has enough context to decide when and how to call it 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 coverage is already 100%, so the baseline is 3, but the description adds substantial meaning beyond the schema: it defines per-type parameter semantics for company vs drug, lists accepted input formats (ticker, CIK, ISIN, name), and warns with a concrete bond example to pass only the entity name and not the full noun phrase. This is exactly the kind of parameter-level nuance an agent needs.

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 uses a specific verb ('resolve') and resource ('user-spoken NAME to canonical/official identifiers'), and it enumerates the exact supported types and identifier sources. It also positions itself as the prerequisite for other tools requiring IDs, making it easy to distinguish from sibling tools like entity_profile or compare_entities.

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 a clear trigger rule: 'Use FIRST whenever you have a name but need an ID,' supplemented by realistic query examples and type-specific guidance. It does not explicitly name sibling alternatives or state when not to use the tool, so it stops just short of full exclusionary guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All zenquotes toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, and the heavy overlap between ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research is mitigated by detailed usage guidance. A few pairs like ai_visibility_check vs scan_competitor_ai_presence or discover_tools vs suggest_questions could confuse an agent, but the descriptions generally draw clear boundaries.

Naming Consistency3/5

Names are all lowercase snake_case and several families are consistent (ask_pipeworx*, polymarket_*), but the overall set mixes styles: verb_noun such as list_quotes and resolve_entity, adjectival noun phrases such as random_quote and today_quote, and bare nouns like entity_profile and recent_changes. The patterns are readable but not predictable.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many,' and the server name zenquotes suggests a much smaller quote-focused surface. The count is defensible for a broad data gateway, but as a unified server it feels overstuffed with many unrelated feature areas.

Completeness4/5

The combined surface is quite complete for its apparent scope: quote retrieval, query/research, grounding, entity resolution, comparisons, change feeds, subscriptions, memory, and prediction-market analysis are all covered. Minor gaps exist, such as no direct quote search and no explicit tool for fetching a pipeworx:// citation URI.

Resources