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Glama

Jpl Ssd

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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already state read-only, open-world, and idempotent. The description adds crucial behavioral details: the exact verdict set, the use of verbatim evidence with pipeworx:// citations, and the crucial distinction between 'could_not_verify' (verification did not happen due to error) and 'unsupported' (no source exists). This goes far beyond annotations and prevents serious misinterpretation.

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 comprehensive yet tightly packed. It front-loads with trigger phrases and a clear statement of purpose, then methodically covers use cases, fallback behavior, return values, and critical caveats. Every sentence adds meaningful information without redundancy, making it longer but efficient for a complex tool.

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?

This tool has no output schema, so the description must explain what the caller receives. It enumerates all possible verdicts, mentions the included pipeworx:// citation, describes reasoning, and explicitly warns about the semantics of 'could_not_verify' and 'unsupported'. Given the tool's complexity and lack of an output schema, this description is fully complete.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by explaining how tolerance_pct overrides the implied wording, its intended range, and specific use case (hallucination detection). It also clarifies the default behavior ('capped at 5'), which is not fully captured in the schema. The claim parameter is exemplified but the description doesn't need to add more.

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 explicit natural-language triggers ('Is it true that…', 'fact check', 'verify the claim that…'), immediately establishing the tool's purpose as claim verification. It clearly distinguishes itself from other tools by specifying the two execution paths (SEC EDGAR for financial claims, grounded pipeline for everything else) and describing the output (verdict + actual value + citation). This is a specific verb+resource with strong sibling differentiation.

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?

It provides explicit when-to-use guidance: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the routing logic for company-financial vs. other claims and notes that it replaces 4–6 sequential calls, giving a clear sense of when this tool is the right choice over a multi-step alternative.

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

A3.5/5.0
Disambiguation2/5

Multiple tools overlap in purpose, especially the ask_pipeworx variants and the polymarket_* tools. An agent would struggle to distinguish between similar functions, increasing the risk of selecting the wrong tool.

Naming Consistency2/5

Tool names use inconsistent conventions: some are snake_case (close_approaches), some are verb_noun (generate_llms_txt), and others mix styles (ask_pipeworx vs. deep_research). No clear pattern is maintained across the set.

Tool Count1/5

At 35 tools, the server is bloated and unfocused. The majority of tools are unrelated to the 'Jpl Ssd' domain, which only has 4 relevant tools. The count is far too high for the stated purpose.

Completeness2/5

The JPL SSD coverage is minimal (only 4 tools), missing key functionalities like detailed object queries or bulk downloads. The remaining tools cover unrelated domains, so the surface is both incomplete for its primary domain and cluttered with off-topic tools.

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