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Glama

Datalastic

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

A4.9/5.0
Behavior5/5

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

The description richly details behavior beyond the read-only, idempotent, and open-world annotations: it discloses that multi-match instruments return figi_candidates without asserting, that unresolved identifiers are explicitly listed under unresolved, and that LEI/FIGI enrichment degrades gracefully if upstream sources are unavailable. This exceeds what annotations convey.

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 every sentence carries unique functional information—from examples to edge-case handling. It is front-loaded with the primary use case and then details specifics. It could be slightly tightened, but the complexity of the tool justifies the length.

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 has no output schema, the description compensates by explaining what is returned (identifiers with source labels, figi_candidates on ambiguity, unresolved list). It covers supported entity types, input variants, error handling, and graceful degradation, leaving no critical gap for an agent to 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?

Although the schema covers 100% of parameters, the description adds substantial semantic depth: for 'value' it clarifies acceptable input formats (ticker, CIK, ISIN, name for company; brand/generic for drug), explains ISIN-to-LEI resolution nuance, and provides a cautionary example about bond names. This goes well beyond the schema's terse descriptions.

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 example queries ('What's the ticker for…', 'find the CIK for…') and states the core function: resolving user-spoken names to canonical identifiers. It clearly distinguishes itself from siblings by emphasizing it provides IDs other tools require as input, making its purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly directs agents to 'Use FIRST whenever you have a name but need an ID.' It also provides situational guidance for supported types (company/drug), warns about how to pass bond issuer names without trailing security-class words, and notes that it replaces 2-3 manual lookups. This is strong actionable 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

A3.7/5.0
Disambiguation2/5

Several tools occupy nearly the same question-answering role: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all accept natural-language factual queries, and ask_pipeworx_beta is explicitly identical to ask_pipeworx today. The polymarket_* family also has overlapping opportunity-detection responsibilities, so an agent can easily select a near-duplicate tool for the same intent.

Naming Consistency4/5

Tool names are uniformly lowercase snake_case and use recognizable domain prefixes like ask_pipeworx_, polymarket_, datalastic_, and pipeworx_, which makes the set easy to group. The main deviation is that some names are verb-first (ask, compare, subscribe) while others are noun phrases (entity_profile, recent_alerts, bet_research), but this is minor and readable.

Tool Count2/5

At 33 tools this exceeds the 25+ threshold for a single server, and many of them are meta-tools or overlapping research/opportunity scanners that could be consolidated. The breadth is justified only partially; several utilities like generate_llms_txt and scan_dependency feel bolted on.

Completeness3/5

As a general research and monitoring toolkit it covers the full life cycle: discover, ask, verify, compare, profile, cite, remember, subscribe, and alert. But the server is named Datalastic yet the maritime surface is thin (only live position and radius lookup, no historical tracking, port calls, or fleet tools), and a few one-off utilities don't fit a coherent domain.

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