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Pypi

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. Changed3 schema fields changed
    • changedInput schema / properties / type / description
      Previous value: -"Entity type. v1 supports \"company\"."New value: +"Entity type: \"company\" or \"drug\"."
    • changedInput schema / properties / type / enum
      Previous value: -[
-  "company"
-]New value: +[
+  "company",
+  "drug"
+]
    • changedInput schema / properties / value / description
      Previous value: -"Ticker, CIK, or company name (e.g., \"AAPL\", \"0000320193\", \"Apple\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."
  3. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses ambiguity handling via figi_candidates, explicit unresolved fields, graceful degradation when GLEIF/OpenFIGI are unavailable, and internal cascading across lookup endpoints. These behaviors materially inform an agent's expectations without contradicting the 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 tool is complex and most clauses earn their place, but the company-type description is a dense one-paragraph run-on with nested parentheses that would benefit from bullets or shorter sentences. It is front-loaded with purpose and usage, so it is not merely verbose.

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 still explains key output behaviors—canonical identifiers, source labels, unresolved fields, figi_candidates on ambiguity, and drug citation format. It covers supported inputs, failure modes, and fallback behavior, making it sufficient for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes both parameters well, so the baseline is 3; the description adds the ISIN-as-company-input behavior and explains non-equity instrument resolution with issuer-name caveats. That is useful extra meaning, though some value guidance is duplicated from the schema.

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 concrete user-phrased examples and states a specific verb-resource pairing: resolve user-spoken names to canonical/official identifiers that other tools need. It then enumerates supported entity types, so an agent can distinguish this entry-point resolver from broad research/profile 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?

The description explicitly says to use this tool FIRST whenever a name is known but an ID is needed, and lists which name-like inputs are accepted. It does not name alternatives or state when not to use it (e.g., when a full entity profile is wanted), so it earns a 4 rather than a 5.

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 have overlapping or near-identical purposes: ask_pipeworx and ask_pipeworx_beta are currently exactly the same behavior, and polymarket_edges, polymarket_arbitrage, and bet_research all present as 'find a betting opportunity' scanners. The detailed descriptions help, but an agent still needs careful triage to avoid picking the wrong entry point.

Naming Consistency3/5

The set is uniformly snake_case and many tools follow verb_noun (get_package, list_releases, resolve_entity), but conventions are split between product-prefixed families (ask_pipeworx, pipeworx_feedback, polymarket_*), noun-led names (entity_profile, recent_alerts, deep_research), and bare-verb memory tools (remember, recall, forget). The result is readable but not a single predictable pattern.

Tool Count2/5

35 tools is well into the overgrown range, and the count is especially mismatched for a server labeled Pypi: only a handful of tools actually relate to Python packages, while the rest cover Pipeworx data lookup, prediction markets, AI visibility, memory, and subscriptions. This looks like several unrelated tool surfaces merged under one server.

Completeness3/5

For the broad Pipeworx data-research surface, coverage is strong: lookup, grounded answers, deep research, entity profiles, comparisons, validation, subscriptions, and memory are all represented. However, for a PyPI-focused server the surface is incomplete—there is no package search, upload, or account/maintainer tooling—and the PyPI tools feel like an afterthought.

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