Skip to main content
Glama

jokes

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?

Beyond the annotations (read-only, open-world, idempotent), the description discloses the embedding model (BGE-base-en), chunking strategy (500-char overlapping windows), and a hard cap of 200K chars with truncation flagged. It also explains return payload includes character offsets for verification, adding behavioral depth not present in structured metadata.

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?

The description is dense but well-organized, front-loading the core purpose and using short clauses. Every sentence adds information (mechanics, limit, pairing), with no filler. It is appropriately sized for the tool's complexity.

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?

The description covers purpose, usage scenario, technical internals, input limits, and output format (passages with offsets and scores). It also provides an integration point with a sibling tool, making it complete for an agent to invoke correctly without an output schema.

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 already provides 100% coverage with descriptions for all three parameters, including default and range for limit. The description adds contextual examples for text ('SEC 10-K body') but does not significantly expand semantics beyond the schema. 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: semantic search inside an already-fetched record, returning top passages with offsets and scores. It uses specific verbs ('search inside') and distinguishes from siblings by explicitly mentioning pairing with ask_pipeworx_grounded. This is specific and unambiguous.

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?

The description explicitly explains when to use: when the record is too big to fit in context, saving context by returning only relevant passages. It also provides a usage pairing with ask_pipeworx_grounded, creating an explicit workflow. This is more than just implied usage.

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

All jokes 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

B3.3/5.0
Disambiguation1/5

The server contains 4 joke-related tools and 31 tools from the Pipeworx data ecosystem, which are entirely unrelated. An agent cannot easily distinguish whether to use a joke tool or a data tool, causing extreme ambiguity.

Naming Consistency2/5

Tool names within the joke subset (e.g., get_joke, search_jokes) and Pipeworx subset (e.g., ask_pipeworx, entity_profile) are individually consistent, but the overall set has no unified naming pattern or domain signal, making the server's purpose unclear.

Tool Count1/5

With 35 tools, the count is excessive for a jokes-themed server. Only 4 tools are joke-related; the remaining 31 belong to a completely different domain, making the tool count highly inappropriate.

Completeness1/5

The joke coverage (get, search, categories, flags) is minimal but functional. However, the server as a whole is a Frankenstein of unrelated domains, lacking a coherent surface. The overwhelming majority of tools are irrelevant to the server name.

Resources