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Glama

Altos

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

A5/5.0
Behavior5/5

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

The description goes well beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) by detailing internal routing, verbatim evidence usage, and the exact semantics of nuanced verdicts (could_not_verify vs. unsupported). It also warns that could_not_verify must not be presented as evidence for or against the claim, which is critical behavioral context.

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 every sentence carries value: trigger phrases, usage rule, path breakdown, return types, and caller warnings. It is front-loaded with the most important information and structured logically, avoiding fluff despite its length.

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 (multi-path verification, nuanced verdicts, no output schema), the description fully covers what the agent can expect: verdict list, actual value with citation, reasoning, and the distinction between verification failure and lack of coverage. It also explains the performance benefit of replacing sequential calls, which helps the agent understand when to prefer this tool.

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

Parameters5/5

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

Although schema coverage is 100%, the description significantly enriches both parameters. It provides concrete examples for 'claim' and explains that 'tolerance_pct' overrides the wording-implied tolerance, with typical values for hallucination detection and the default behavior. This adds decision-making context beyond the schema definitions.

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 uses specific verbs like 'check whether something a user said is factually correct' and clearly identifies the resource ('natural-language claim verification against authoritative sources'). It distinguishes this tool from siblings by explaining the verdict-based output and the two distinct verification paths (SEC EDGAR + XBRL for company finance, grounded pipeline for everything else).

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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct.' It also provides clear sub-guidance for company-financial claims vs. any other factual claim, and explains the critical handling of 'could_not_verify' and 'unsupported' outcomes, which effectively tells callers what to do and not do with the result.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes; however, 'ask_pipeworx' and 'ask_pipeworx_grounded' are very similar, and 'polymarket_edges' vs 'polymarket_edge_tracker' could cause confusion. The real estate tools (altos_active_listings, altos_new_listings, altos_pending_sales) are clearly differentiated by status.

Naming Consistency4/5

Tool names use snake_case and are descriptive, but prefixes vary (altos_, polymarket_, ask_pipeworx, etc.) and patterns like 'entity_profile' or 'generate_llms_txt' don't follow strict verb_noun. Overall, naming is fairly consistent and readable.

Tool Count3/5

36 tools is high but justifiable given the broad scope (real estate, prediction markets, SEC/FDA data, etc.). Some utility tools (list_subscriptions, pipeworx_feedback) could be integrated, but the count is borderline heavy for a single server.

Completeness4/5

The server covers multiple domains thoroughly with tools for real estate, company profiles, prediction markets, and general data queries. Minor gaps exist (e.g., no dedicated tool for FDA drug details beyond ask_pipeworx), but meta-tools like deep_research fill many needs.

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