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Glama

Onedrive

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

A5/5.0
Behavior5/5

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

Annotations already provide readOnly/openWorld/idempotent hints, and the description enriches them with concrete behavioral details: the two execution paths, the exact verdict vocabulary, the inclusion of a pipeworx:// citation, and the crucial caller-facing warning that could_not_verify is not evidence. This adds significant value beyond structured hints, with no contradiction.

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 long but densely packed. It is front-loaded with trigger examples and core purpose, then logically flows through routing, return values, and caveats. Each sentence contributes operational value, so the length is justified for a multi-path, high-stakes verification 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?

With no output schema, the description fully enumerates the possible verdicts (confirmed, approximately_correct, refuted, inconclusive, unsupported, could_not_verify), explains the difference between unsupported and could_not_verify, and describes the error object. It also positions the tool as a replacement for a multi-step pipeline, offering complete context for an agent.

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?

Schema descriptions cover both parameters at 100%, and the description goes further by explaining tolerance_pct semantics: it overrides the wording-implied tolerance, recommends 1–2 for hallucination detection, and notes the default cap of 5. This practical guidance is exactly the kind of meaning the schema alone cannot convey.

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 immediately provides natural-language trigger phrases and states 'natural-language claim verification against authoritative sources', clearly specifying the tool's function and unique role. It also distinguishes between financial and non-financial claims, which sets it apart from sibling tools like ask_pipeworx_grounded or deep_research.

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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct' and gives routing rules for company-financial vs. other claims. It also explains that it replaces 4–6 sequential calls, providing clear context on when to prefer this composite tool over chaining simpler lookups.

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

A3.6/5.0
Disambiguation2/5

The set mixes near-synonymous routers (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), overlapping discovery tools (discover_tools, suggest_questions), and several prediction-market scanners (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker) whose boundaries are easy to blur. The six onedrive_* tools are distinct, but they are buried in an unrelated toolkit where multiple tools appear to address the same task.

Naming Consistency2/5

Naming is split across several conventions: onedrive_*, polymarket_*, and pipeworx_* form consistent clusters, but top-level tools use bare verbs (remember, recall, forget), noun phrases (entity_profile, compare_entities), and varied styles (deep_research, generate_llms_txt, validate_claim). The pattern is readable within clusters, but not predictable across the server.

Tool Count2/5

37 tools is heavy, and the vast majority have nothing to do with the server's name 'Onedrive' — only 6 of 37 target OneDrive, while 31 span Pipeworx data, Polymarket betting, memory, and web utilities. This is a sprawling multi-domain bundle rather than a focused server.

Completeness1/5

As a OneDrive server, the surface is severely incomplete: it offers read-only coverage (list, search, get, profile, shared) but no upload, create, update, move, copy, delete, or share operations, and binary Office/PDF content returns unreadable bytes. The Pipeworx tools are individually comprehensive, but they do not fill the basic lifecycle gaps for the server's apparent file-management domain.

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