Skip to main content
Glama

Landprice

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

A4.8/5.0
Behavior5/5

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

Beyond annotations, the description discloses critical behavioral details: it lists all verdict types, defines could_not_verify as a pipeline failure (with verification_error{stage,detail}) explicitly warning it is NOT evidence, explains unsupported as no source coverage, and mentions verbatim evidence with pipeworx:// citations. This goes far beyond the readOnly/openWorld/idempotent hints.

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 each section earns its place: trigger phrases, purpose, routing, return value, error semantics, and efficiency rationale. It is well-structured and front-loaded with the most identifying information, though it could be trimmed slightly without losing critical 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?

Given there is no output schema, the description must explain return values, which it does thoroughly (verdicts, actual value, citation, reasoning). It also covers the two main claim categories, error handling, and internal pipeline steps, making it fully self-contained for an agent to understand what will happen and what to do with the result.

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%, so baseline is 3. The description adds extra meaning for tolerance_pct: it explains it overrides the tolerance implied by the claim wording, can be set low for hallucination detection, and defaults to a cap of 5. The claim parameter is also illustrated with examples, enriching the schema's already good documentation.

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+resource (verify natural-language claims against authoritative sources) and includes trigger phrases for common variations. It clearly distinguishes from siblings by detailing the SEC EDGAR fast path for financial claims and the grounded pipeline for other claims, and it names the verdict output, making its function unmistakable.

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 when to use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also explains the two routing paths (financial vs. other) and notes that it replaces 4–6 sequential calls, giving clear context on when this tool is more suitable than chaining lower-level operations.

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

All Landprice 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

A3.9/5.0
Disambiguation2/5

Several tool clusters have unclear boundaries. `ask_pipeworx_beta` is explicitly described as currently identical to `ask_pipeworx`, `discover_tools` overlaps with `suggest_questions`, and the six Polymarket tools (`bet_research`, `polymarket_edges`, `polymarket_arbitrage`, etc.) blur together for opportunity-finding. An agent would struggle to pick the right tool without reading every description carefully.

Naming Consistency4/5

Naming is overwhelmingly consistent snake_case with a verb_noun or noun pattern (`ask_pipeworx`, `list_subscriptions`, `validate_claim`, `recent_changes`). The `polymarket_*` and `ask_pipeworx_*` families follow clear conventions. Minor deviations like `bet_research`, `entity_profile`, and `landprice_points` being noun-first are still readable and predictable.

Tool Count2/5

32 tools is heavy for a server named 'Landprice' when exactly one tool (`landprice_points`) actually concerns land prices. The vast majority of tools constitute an unrelated general-purpose data research and prediction-market platform, making the count feel bloated and scattershot relative to the server's stated purpose.

Completeness3/5

For the actual broad scope revealed by the tools — structured data lookup, grounded verification, deep research, entity resolution, comparison, monitoring, and memory — the surface is reasonably complete with no obvious dead ends. However, for the 'Landprice' domain implied by the server name, coverage is nearly absent: only Japan is covered, with no other countries, address search, or property-level data.

Resources