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Csv

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 mark the tool read-only, open-world, and idempotent. The description adds substantial behavior beyond annotations: internal cascade across multiple lookup endpoints, graceful degradation when GLEIF/OpenFIGI are unavailable, explicit `unresolved` identifiers, ambiguity returning `figi_candidates`, ISIN-to-LEI mapping for non-US issuers, and support for non-equity instruments. 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 densely informative, with trigger phrases and core purpose front-loaded before the detailed type breakdown. While nested parentheticals make it somewhat heavy, every major section earns its place given the tool's multi-source complexity.

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?

Despite having no output schema, the description covers inputs, supported types, resolution sources, ambiguity behavior, unresolved-identifier handling, graceful degradation, and output labeling. For a tool of this complexity, nothing critical is missing for an agent to select and invoke 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 100%, but the description goes well beyond the schema: it explains that ISIN is a valid company input, distinguishes exact-ticker mapping from name search, and warns that only the entity name should be passed (not the full noun phrase). This adds meaningful decision-making context for how to populate the `value` parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('resolve') and resource ('user-spoken NAME to canonical/official identifiers'), supported by concrete trigger examples like 'What's the ticker for…' and 'find the CIK for…'. It does not explicitly name or differentiate sibling tools such as entity_profile or compare_entities, but it firmly establishes itself as the ID-resolution entry point.

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 usage guidance: 'Use FIRST whenever you have a name but need an ID' and provides trigger phrases, supported entity types, and input constraints like passing only the entity name. It lacks explicit when-not-to-use guidance or named alternatives, but the context is strong and actionable.

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
Disambiguation3/5

Several tools overlap in purpose: ask_pipeworx_beta is explicitly identical to ask_pipeworx today, and the polymarket_* family plus bet_research all touch prediction-market analysis. The descriptions are extremely detailed and mostly disambiguate, but an agent must rely on very long text to avoid misselection.

Naming Consistency3/5

Names are almost all snake_case and readable, but the pattern is mixed: some are verb-first (resolve_entity, list_subscriptions), some noun-first (entity_profile, polymarket_edges), and some are one-word verbs (remember, forget). The ask_pipeworx_* and recent_* prefixes are consistent, but there is no single verb_noun convention throughout.

Tool Count2/5

33 tools is well over the 25+ threshold, especially for a server named 'Csv' that contains only two CSV-specific tools. The rest spans several unrelated domains: data research, prediction markets, subscriptions, memory, AI visibility, and package scanning. The set feels like multiple servers bundled together rather than one well-scoped surface.

Completeness4/5

As a broad data-research platform, coverage is strong: lookup, grounded answers, deep research, entity resolution, profiles, comparisons, fact-checking, subscription lifecycle, and memory persistence are all present. Minor gaps exist, such as no direct fetch tool for pipeworx:// citation URIs and no subscription-update tool, but most workflows have no dead ends.

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