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Glama

Close Crm

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.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, but the description adds crucial behavioral context: the meaning of could_not_verify (check did not happen, not evidence), unsupported (no source coverage), the verification_error payload, and the guaranteed citation/reasoning. This goes well beyond what annotations provide.

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 dense but front-loaded with trigger phrases and a clear main definition. Every sentence contributes behavioral or usage value; the only minor inefficiency is the slight overlap between the 'Replaces 4–6 sequential calls' phrase and the routing details.

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?

Because there is no output schema, the description correctly takes responsibility for explaining return values: the verdict enum, actual value with pipeworx:// citation, reasoning, and the verification_error attribute. It also covers both execution modes and edge-case semantics, making it self-sufficient.

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% for both parameters with examples. The description adds meaningful semantics beyond schema by explaining that tolerance_pct overrides the claim-wording threshold, caps at 5%, and recommending 1–2% for hallucination detection. This enriches the schema-only understanding.

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 states a specific verb ('verify') and resource ('natural-language claim') along with concrete trigger phrases. It clearly distinguishes the company-financial structured path from the grounded fallback for any other factual claim, separating it from generic research or Q&A 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 says 'Use whenever the agent needs to check whether something a user said is factually correct' and provides clear routing rules for financial vs. non-financial claims. However, it does not name sibling tools as alternatives or state what should not use this tool for, so it lacks explicit exclusions.

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
Disambiguation2/5

Multiple Pipeworx tools overlap significantly (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities, recent_changes all query similar data). Close CRM tools are distinct but the overall set mixes two domains, causing confusion.

Naming Consistency2/5

Tool names mix snakes (close_get_lead), lowercase (ask_pipeworx), and descriptive phrases (scan_competitor_ai_presence). No consistent verb_noun pattern across the entire set.

Tool Count2/5

37 tools is excessive for a focused server. The combination of Close CRM and Pipeworx data services creates scope creep; many tools could be separate servers.

Completeness3/5

Pipeworx tools offer extensive data coverage, but Close CRM lacks update/delete operations for leads, contacts, and opportunities, leaving basic CRUD gaps.

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