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Glama

Httpstatus

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

A4.6/5.0
Behavior5/5

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

Despite annotations already declaring readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the description adds substantial behavioral context: the two execution paths (SEC EDGAR vs. grounded pipeline), the list of possible verdicts, the special meaning of 'could_not_verify' (error, not evidence) and 'unsupported' (no source coverage), and the presence of verification_error{stage,detail}. This goes well beyond the annotation baseline.

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 but dense, with all sentences contributing behavioral guidance or scope. It opens with user-phrase examples, then gives the primary use case, routing details, return value structure, and critical caveats. It is structured and front-loaded, though slightly verbose—but the length is justified by the tool's complexity.

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 lack of an output schema, the description thoroughly covers the return format (verdict list, actual value with citation, reasoning) and the critical semantics of failure states. It also explains the internal pipeline and why the tool replaces multiple sequential calls. No major gaps remain for an agent to use it correctly.

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 description coverage is 100% with good parameter descriptions, so the baseline is 3. The tool description adds extra value by explaining how tolerance_pct overrides the default and recommending values for hallucination detection, and by providing example claims that clarify the expected format. This exceeds the schema baseline.

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 a natural-language claim verification function with specific trigger phrases ('fact check', 'verify the claim that…'), and states it returns a verdict against authoritative sources. It distinguishes itself from sibling tools by focusing on factual claim checking rather than general research or entity lookup.

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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and explains the routing logic: company-financial claims use the SEC EDGAR fast path, while other claims fall through to the grounded pipeline. It also notes the tool replaces 4–6 sequential calls, providing clear context. However, it does not name a specific alternative tool for when not to use it.

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
Disambiguation4/5

Most tools have distinct purposes, but ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very similar, causing potential confusion. The memory tools (remember/recall/forget) are separate, but the overall set is diverse enough that agents can typically distinguish them.

Naming Consistency2/5

Tool names use a mix of camelCase (ask_pipeworx, entity_profile, generate_llms_txt) and snake_case (http_status, list_http_statuses, list_subscriptions), with no consistent verb_noun pattern. Some names are descriptive, but the lack of a unified convention hurts predictability.

Tool Count2/5

33 tools is high for a server named 'Httpstatus' that only has two HTTP-related tools. The large number spans data retrieval, memory, subscriptions, prediction markets, and more, making the server feel bloated and unfocused.

Completeness3/5

The server covers many domains but each is incomplete. HTTP status has lookup and list but no lifecycle. Memory tools are basic. Data retrieval is extensive but not exhaustive for any single domain. It's a broad but shallow collection.

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