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chucknorris

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations declare readOnly, openWorld, idempotent, non-destructive. Description adds substantial behavioral detail: top-N passages with character offsets and similarity scores, BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, and a 200K char cap with truncation flag. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, perfectly front-loaded with the core purpose. Each sentence adds value: use case, output details, pairing, and technical implementation. No fluff or redundancy.

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?

For a tool with 3 params and no output schema, the description covers inputs, outputs (passages with offsets/scores), use case, limitations (truncation), and integration with the gateway. Everything an agent needs to invoke correctly is present.

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 covers 100% of params, so baseline is 3. Description adds real-world usage context for text ("a SEC 10-K body, an article, a long tool result") and query examples, plus mentions limit defaults. This adds meaningful semantic color beyond the schema, warranting a 4.

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?

Purpose is crystal clear: "Semantic search INSIDE a fetched record" with concrete examples (SEC filing, article, long tool result). It distinguishes from siblings by explicitly framing search as operating on already-fetched content and pairing with ask_pipeworx_grounded.

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?

Explicit "Use when the record is too big to cram into the prompt" and a complementary alternative: "Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document." No explicit when-not, but clear context indicates when this tool is appropriate.

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

A3.5/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical query routers, and the five polymarket_* tools all hunt mispricings in subtly different ways. The memory trio (remember/recall/forget) and subscription trio (subscribe/unsubscribe/recent_alerts) are distinct, but the many data-query tools create frequent ambiguity for an agent deciding which one to call.

Naming Consistency2/5

Naming is a mix of verb_noun (list_categories, resolve_entity, validate_claim), bare nouns (entity_profile, random_joke, deep_research), single verbs (forget, recall), and brand-prefixed nouns (pipeworx_feedback, pipeworx_trending). There's no consistent pattern across the set, so an agent cannot predict a tool's name from its function.

Tool Count1/5

35 tools for a server named 'chucknorris' is an extreme mismatch; only 4 tools actually relate to Chuck Norris jokes. The rest form a sprawling collection of data-research, prediction-market, subscription, and memory utilities that have nothing to do with the stated server identity and overwhelm any agent expecting a simple joke API.

Completeness2/5

The Chuck Norris joke subset is complete (random, by-category, search, categories), but the overall server attempts many unrelated domains—structured data queries, prediction-market arb, entity profiles, subscriptions, memory—none of which are clearly scoped or fully coherent. The result is a grab-bag with no single domain that feels finished, and the incongruous inclusion of joke tools adds confusion rather than coverage.

Resources