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Glama

Microsoft Todo

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

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld), the description details the processing pipeline, the list of verdicts, the distinction between 'could_not_verify' and 'unsupported', and the critical warning that 'could_not_verify' must not be treated as evidence. This goes well beyond the structured annotations.

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 well-structured and front-loaded with examples and usage. Each segment (routing, return values, error warnings) contributes necessary information; while it could be tightened, it is not overly verbose.

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, the description covers the use cases, internal routing, return contract, error semantics, and the performance benefit of replacing multiple sequential calls. It provides ample context for an agent to invoke the tool correctly, even without an output schema.

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

Parameters3/5

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

The input schema already provides thorough descriptions for both parameters (claim and tolerance_pct) with 100% coverage. The description adds examples of claims but does not add parameter-specific guidance beyond the schema, such as how to set tolerance for strict checking. It only references tolerance implicitly through the verdict 'approximately_correct'.

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's function: verifying natural-language factual claims against authoritative sources. It includes concrete query phrasings and distinguishes between company-financial and other claims, which differentiates it from sibling tools like ask_pipeworx or search 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 provides example phrases for triggering the tool. It explains the two routing paths but does not name specific sibling tools as alternatives or call out when not to use this tool, though the context is clear.

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

B3.4/5.0
Disambiguation1/5

The set is dominated by overlapping Pipeworx and prediction-market tools, with ask_pipeworx and ask_pipeworx_beta explicitly described as currently identical, and ask_pipeworx_grounded, deep_research, and discover_tools serving heavily overlapping lookup purposes. The four Microsoft To Do tools are buried among 31 unrelated tools, making it very hard for an agent to select the right tool for the server's apparent domain.

Naming Consistency2/5

Most names are snake_case, but the naming conventions are otherwise mixed: there are verb_noun tools like list_tasks and get_task, bare verbs like remember/forget/recall, prefixed families like polymarket_*, and noun-phrase names like entity_profile, recent_changes, and pipeworx_trending. The lack of a consistent pattern across the set, especially relative to the Microsoft To Do server name, makes naming unpredictable.

Tool Count1/5

35 tools is already heavy, but the bigger problem is that only 4 of them are for Microsoft To Do, the server's stated name and purpose. The remaining 31 tools belong to unrelated Pipeworx data, prediction-market, and memory-management domains, which is an extreme scope mismatch.

Completeness1/5

For a Microsoft To Do server, the surface is severely incomplete: list_task_lists, list_tasks, get_task, and find_due_tasks cover reading and browsing only, with no create, update, complete, or delete operations. Even the broader Pipeworx functionality is scattered and redundant rather than forming a coherent domain surface.

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