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Glama

String Db

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 mark the tool read-only and idempotent, but the description adds substantial behavioral context: graceful degradation when GLEIF/OpenFIGI is unavailable, non-equity instrument resolution, ambiguous results returning figi_candidates, unresolved identifiers being listed explicitly, and internal cascading of multiple lookups. These details are beyond any structured annotation and materially shape agent expectations.

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 and dense, but the information is high-value and organized by query examples, supported types, and behavioral caveats. The key instruction 'Use FIRST' is front-loaded. It is not as tight as a two-sentence definition, but it earns most of its length by explaining ambiguity and fallback behavior.

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, the absence of an output schema, and the range of entity types, the description is remarkably complete. It covers return behavior for each type, ambiguity handling, unresolved identifiers, enrichment source failure, and input formatting pitfalls. An agent has enough context to invoke the tool correctly and interpret likely responses.

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%, so the schema already documents both parameters. The description goes further by adding critical usage nuance: pass only the entity name, never the full noun phrase, with a concrete example of how trailing security-class words break the FIGI lookup. It also clarifies how each entity type maps to identifiers and sources.

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 states a specific purpose: resolve user-spoken names to canonical/official identifiers. It opens with concrete query examples and enumerates supported entity types and identifier sources, making the tool's job unmistakable. It also distinguishes itself from sibling lookup tools by explicitly noting it is the first step when a name needs to become an ID.

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 usage directive: 'Use FIRST whenever you have a name but need an ID.' It clearly establishes when to use the tool and what inputs are appropriate. However, it does not explicitly describe when not to use it or name alternative tools, so it falls short of full exclusion guidance.

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

B3.2/5.0
Disambiguation2/5

Many tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying data catalog. The six polymarket_* tools also blur together (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread), and resolve vs resolve_entity is an outright collision an agent will likely misselect.

Naming Consistency3/5

There is a solid verb_noun core (list_subscriptions, scan_dependency, validate_claim, suggest_questions, compare_entities) but it is mixed with bare nouns (enrichment, homology, interactions, network) and product-prefixed names (pipeworx_feedback, polymarket_edges, ask_pipeworx). No single consistent pattern holds across the set, though the clusters are internally predictable.

Tool Count2/5

At 36 tools this is well above the 25+ threshold for 'too many,' and the sprawl is not justified by a single coherent domain—prediction markets, bioinformatics, brand visibility, npm scanning, and subscription management are jammed together. The count makes the tool surface hard to navigate even with good descriptions.

Completeness4/5

Within each major cluster the lifecycle feels covered: memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list/recent_alerts), STRING-DB (resolve/homology/interactions/network/enrichment), and Polymarket analysis (scan/edge/arb/fill-risk/track) all form reasonably complete workflows. The main gap is that the server attempts so many domains that none is exhaustively deep, but there are no critical dead ends.

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