Skip to main content
Glama

Arcgis Tigard

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

A4.4/5.0
Behavior5/5

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

The description goes far beyond the read-only and idempotent annotations. It details the dual verification paths (structured vs. grounded), explains the meaning of each verdict type, and explicitly warns callers that 'could_not_verify' does not constitute evidence. It also discloses the return structure (verdict, actual value, citation, reasoning) and the special handling of unsupported claims. This is exemplary behavioral disclosure.

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 somewhat lengthy, but every sentence contributes value: examples, scope, behavior, warning about could_not_verify, and efficiency note. It is structured with clear sections and front-loads the purpose. Minor redundancy exists (repeating the meaning of unsupported), but overall it is well-organized and not bloated.

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 and the absence of an output schema, the description covers all necessary aspects: use cases, processing paths, return values (verdict, value, citation, reasoning), and edge-case semantics (could_not_verify vs. unsupported). It also explains the benefit over sequential calls, making the tool self-contained for correct invocation.

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 schema covers both parameters completely (100% coverage) with detailed descriptions, including examples for 'claim' and the purpose of 'tolerance_pct'. The description adds no additional parameter-specific meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

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 states the tool's function: natural-language claim verification against authoritative sources, with specific examples of user queries. It distinguishes itself from sibling tools by describing its role in checking factual correctness and its two processing paths (SEC EDGAR for financial claims, grounded pipeline for others). The verb+resource is specific: 'verify claims'.

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 tells the agent when to use this tool ('Use whenever the agent needs to check whether something a user said is factually correct') and highlights its efficiency ('Replaces 4–6 sequential calls'). It does not explicitly name alternative tools, but the usage context is clear and the exclusion of when-not-to-use is implied by the automatic fall-through behavior.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Arcgis Tigard toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the various ask_pipeworx variants and entity research tools (entity_profile, compare_entities, recent_changes). The subtle differences between ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are likely to cause agent misselection.

Naming Consistency2/5

Tool names use inconsistent patterns: snake_case (ask_pipeworx, query_layer) mixed with descriptive phrases (ai_visibility_check, generate_llms_txt) and no clear verb_noun structure. Some names are vague (process, run) though those are absent here; overall naming is arbitrary.

Tool Count2/5

34 tools is too many for an Arcgis Tigard server. The majority are generic Pipeworx data query tools (27+ tools) that have little to do with ArcGIS, making the tool count feel bloated and unfocused for the server's stated purpose.

Completeness1/5

The server severely lacks ArcGIS-specific functionality. Only three tools (search_datasets, query_layer, layer_info) are relevant to ArcGIS; the rest are unrelated Pipeworx tools. Essential ArcGIS operations like editing, analysis, or visualization are missing, making the surface incomplete.

Resources