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Glama

Samgov

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.",
+  "type": "number"
+}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, but the description adds substantial behavioral context: it explains the could_not_verify vs unsupported distinction, warns that could_not_verify is not evidence, and discloses the fall-through pipeline and return format with citations and reasoning. This goes well beyond 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.

Conciseness5/5

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

The description is dense but well-organized: trigger phrases, usage context, two processing paths, return values, and a critical caller warning. Every sentence contributes essential information, and the structure (with bullet-like breaks) improves scannability.

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 (two pipelines, six verdicts, error objects) and no output schema, the description covers all necessary semantics: verdict meanings, error handling, citation behavior, and the fact that it replaces multiple sequential calls. It is fully self-contained for correct invocation and interpretation.

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 (100% coverage), and the description augments this with practical guidance: tolerance_pct overrides implied tolerance, suggests 1–2 for hallucination detection, and notes the 5% cap. Claim examples reinforce usage. This adds meaning beyond the schema without fully duplicating it.

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 identifies the tool as natural-language claim verification with an explicit verb ('validate') and resource ('claims'). It enumerates natural-language trigger phrases and describes two processing paths (structured vs grounded), distinguishing it from sibling research/analysis 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?

It explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and gives clear context for when to use the structured vs grounded pipeline. However, it does not explicitly name alternatives or list when-not-to-use scenarios, stopping 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

A3.8/5.0
Disambiguation2/5

Three ask_pipeworx variants and a dense cluster of polymarket_* edge tools have heavily overlapping purposes, and ai_visibility_check vs scan_competitor_ai_presence further blurs boundaries. Only the sam_*, memory, and subscription tools form cleanly distinct families.

Naming Consistency3/5

All names are lowercase snake_case and readable, but the pattern is mixed: verb_noun names (compare_entities, resolve_entity), bare verbs (remember, recall, forget), noun phrases (entity_profile, polymarket_edges), and domain-prefix families (sam_*, polymarket_*) coexist. No camelCase chaos, but no consistent verb style either.

Tool Count2/5

36 tools is well beyond the ideal range, and many are near-duplicates or wrappers (ask_pipeworx variants, ai_visibility_check vs scan_competitor_ai_presence). The server is named Samgov, yet only 5 tools actually concern SAM.gov, making the count feel inflated and unfocused.

Completeness3/5

The SAM.gov subset covers entity search, opportunities, set-asides, opportunity details, and exclusions, but omits major datasets like contract awards. The broader Pipeworx research/memory/subscription surface is extensive, though it is muddled by redundant query modes and lacks a direct way to invoke individual pack tools.

Resources