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Codeforces

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?

Beyond the readOnly/openWorld/idempotent annotations, the description discloses the two verification pipelines (SEC EDGAR + XBRL vs. grounded), the exact verdict vocabulary, and the important distinction between could_not_verify and unsupported. It also warns callers not to interpret could_not_verify as evidence, providing critical behavioral context.

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, starting with example triggers, then usage guidance, pipeline details, return values, and special meanings. Each section serves a purpose, and while slightly verbose, it is not padded with unnecessary content.

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 lack of an output schema, the description thoroughly explains the return type (verdict, value with citation, reasoning) and provides detailed semantics for could_not_verify and unsupported. It also covers both routing paths, making the tool's behavior fully understandable for an agent.

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 100% coverage for both parameters, including descriptions and examples. The tool description adds little beyond referencing percent-delta math and tolerance override, which the schema already states, 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 performs natural-language claim verification against authoritative sources, with a specific verb and resource. It distinguishes itself from siblings by focusing on fact-checking and returning verdicts, and even mentions replacing 4–6 sequential calls, making its purpose unmistakable.

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 provides an explicit instruction to use the tool whenever the agent needs to check factual correctness, and describes the different handling paths for company-financial claims vs. other claims. However, it does not explicitly name alternative tools or provide when-not-to-use scenarios, so it falls short of a 5.

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

B3.2/5.0
Disambiguation2/5

The set mixes Codeforces-specific tools with a large Pipeworx data toolkit. Within the Pipeworx family, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (the beta explicitly matches the stable version today), and deep_research/discover_tools/suggest_questions also overlap as meta entry points, creating real selection hazard. An agent could easily misselect among these.

Naming Consistency2/5

Naming patterns are mixed: some tools use verb_noun (list_subscriptions, validate_claim, resolve_entity), some use bare verbs (remember, forget, recall), and some are noun phrases (problemset, blog_entry_view, recent_actions). Word order also varies (contest_list vs list_subscriptions), so no consistent convention is followed.

Tool Count2/5

39 tools is far too many for a server named 'Codeforces' — only 8 tools actually concern Codeforces, while the remaining 31 are unrelated Pipeworx/utility tools. This bloats the surface and makes the server feel unfocused, especially for an agent expecting a compact Codeforces API.

Completeness3/5

For the Codeforces domain, the core read-only API is covered (contests, standings, problems, user info/rating/status, blog entries, recent actions) but some endpoints are missing (blog comments, rated list, problem statements). The large number of extra tools does not fill these gaps and instead obscures the intended purpose.

Resources