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Glama

LiveDataLink

by io.github.blackboxfoundry

entity_dossier

Read-onlyIdempotent

Build a consolidated cross-source dossier for a company in one call: SEC registration and identifiers (EDGAR), environmental footprint and regulated facilities (EPA ECHO), and sanctions/denied-party screening (OFAC/UN/EU/BIS) with a confidence score. Returns a per-source summary plus top records. This is a single AI-native lookup across data that otherwise lives in separate silos. Matches are name-based; verify identity before relying on any link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCompany / organization name, e.g. 'Chevron Corporation', 'Acme Trucking LLC'.
limitNoMax records to surface per source (default 5, max 15).
stateNoOptional 2-letter US state to disambiguate location-based sources (e.g. 'TX').

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable behavioral context beyond that: matches are name-based, identity should be verified before relying on any link, and the result includes a confidence score plus per-source summaries. This meaningfully helps an agent set expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: the core purpose is in the first sentence, source coverage in the second, and a critical caveat in the last. Every sentence earns its place, and there is no redundant repetition of schema or annotation information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex multi-source tool with no output schema, the description does well by enumerating sources, explaining the return shape (per-source summary plus top records), and flagging the name-matching caveat. It could have added a bit more about what happens on no matches or ambiguity, but the essential operational context is present.

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

Parameters3/5

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

The schema already provides 100% description coverage for all three parameters, so the baseline is 3. The description doesn't add parameter-level detail, but it reinforces that the tool is company-focused and returns top records, which indirectly relates to the limit parameter. This is adequate but not exceptional.

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 a specific verb and object: 'Build a consolidated cross-source dossier for a company in one call.' It then names the exact sources covered (SEC/EDGAR, EPA ECHO, OFAC/UN/EU/BIS), which clearly distinguishes this aggregation tool from single-source siblings like edgar_company_facts or epa_facility_search.

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 clear context for when to use the tool: when a single cross-source consolidated dossier is needed, instead of querying separate data silos. It does not explicitly name alternative tools or say when not to use it, so it stops short of 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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.

Resources