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Glama

Govparse Government Data Gateway

by io.govparse

build_parse

Compose a lead-generation Parse from what you sell and who you target. Returns the signals/legs it watches, a plain-English restatement, a per-Parse narrative, a fit score, the expected new-leads-per-period rate, and a small free sample. This is the FREE preview of get_leads.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parseNoAlternatively, the `parse` object returned by build_parse — replays the exact composed intent so a preview and its paid pull match.
queryNoOPTIONAL value-prop LABEL only (what you sell) — it does NOT select the Parse and is never inferred. The Parse is composed ONLY from an explicit selection: a `preset_id`, or `target_entity` + `buying_moments`.
statesNoTwo-letter US state codes to scope to; empty = nationwide.
industryNoAn industry slug from list_industries (e.g. "freight-factoring", "pharma-sales") — scopes/labels the pick. Not a selector on its own.
preset_idNoAlternatively, a curated-Parse or preset id (from list_industries) to compose a proven Parse.
attributesNoIdeal-customer descriptors, e.g. ["small / emerging", "multi-location"].
target_entityNoStructured pick: the entity you SELL TO. Taken verbatim (never guessed), so a pharma-brand seller can't be mis-targeted onto physicians.
buying_momentsNoStructured pick: library template ids (buying-moment ids) from build_parse/list_industries output. Each maps to a real signal.

Schema Changelog

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

  1. Changed3 schema fields changed
    • changedInput schema / description
      Previous value: -"Describe what you sell (plain `query`) OR pick structured {industry, target_entity, buying_moments, attributes, states} OR pass a `preset_id`."New value: +"Compose a Parse from an EXPLICIT selection (deterministic — no free-text guessing): pass a `preset_id` (a proven curated Parse), OR structured {target_entity, buying_moments[]} (+ optional industry, attributes, states), OR a build_parse `parse` echo. `query` is an optional label only."
    • changedInput schema / properties / industry / description
      Previous value: -"Structured pick: an industry slug from list_industries (e.g. \"freight-factoring\", \"pharma-sales\"). Free text is also accepted."New value: +"An industry slug from list_industries (e.g. \"freight-factoring\", \"pharma-sales\") — scopes/labels the pick. Not a selector on its own."
    • changedInput schema / properties / query / description
      Previous value: -"Plain-English description of what you sell and who you target, e.g. \"I sell factoring to brand-new trucking carriers\". Either this OR the structured fields below."New value: +"OPTIONAL value-prop LABEL only (what you sell) — it does NOT select the Parse and is never inferred. The Parse is composed ONLY from an explicit selection: a `preset_id`, or `target_entity` + `buying_moments`."
  2. Added

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It transparently lists what the tool returns (signals/legs, plain-English restatement, narrative, fit score, expected rate, free sample) and states it is a 'FREE preview', conveying the preview nature. It does not detail limitations like the deterministic selection rule, but the schema description covers that. No contradictions with annotations (none provided).

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 two sentences: the first states purpose and enumerates return values, the second highlights the free-preview relationship to get_leads. It is front-loaded with the primary action and zero filler. Every sentence adds meaningful context.

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?

Given the tool's complexity (8 params, nested objects, no output schema), the description lists return values but not their structure. However, the schema description is extremely rich and supplies the selection logic, making the combined context sufficient for an agent to invoke correctly. The description clearly frames the tool's role relative to get_leads, which is essential context for a preview tool.

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 input schema has 100% parameter coverage with detailed descriptions, including the nested 'parse' object, enums, and the role of 'query'. The tool description adds only a high-level paraphrase ('what you sell and who you target') without new parameter-specific details. Since schema coverage is complete, the baseline of 3 is appropriate, though the description does reinforce the intended user-facing intent.

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 clearly states the verb 'Compose' and the resource 'lead-generation Parse', specifying it transforms 'what you sell and who you target'. It explicitly distinguishes itself from the paid sibling 'get_leads' by calling itself the 'FREE preview'. This makes the tool's purpose immediately clear and unique among the large sibling set.

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 positions build_parse as the free preview of get_leads, implying use for composing and testing before purchasing actual leads. It doesn't explicitly exclude alternatives like list_industries, but the 'FREE preview' framing provides clear context. The schema description adds detailed usage rules (explicit selection via preset_id or target_entity+buying_moments, query as label only), further guiding correct usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Govparse Government Data Gateway toolsInspect
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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct domain and specific action (e.g., FDA approvals vs clearances vs recalls; firmstanding business360 dossier vs search vs screen). Even overlapping concepts like 'business360' vs 'business360_lookup' are distinguished by input (UUID vs name+state). No two tools appear to do the same thing.

Naming Consistency5/5

All tools use a consistent lowercase snake_case pattern with domain prefix (e.g., fda_*, firmstanding_*, fmcsa_*, govcon_*). Action words (search, lookup, screen, feed, stats) follow predictable usage. The naming is uniform and easy to parse.

Tool Count4/5

38 tools is on the higher end but appropriate for a comprehensive government data gateway spanning multiple agencies and datasets. Each domain has a reasonable number of tools (e.g., FMCSA: 7, OFLC: 6). Could potentially be trimmed slightly, but overall well-scoped for the stated purpose.

Completeness5/5

The tool surface covers the major government data sources comprehensively: FDA (approvals, clearances, recalls), FMCSA (carrier census, safety, insurance, etc.), FSIS, DOJ/OFLC, OSHA/EPA/DOL enforcement, SEC insider filings, clinical trials, VA facilities/opportunities/vendors, and federal contracting. No obvious gaps for the stated gateway purpose.

Resources