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Glama

Codewars

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

A4.8/5.0
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds technical details: 'BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs truncated and flagged)' and mentions offset for verification.

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?

Single paragraph front-loads core purpose. Includes necessary details without redundancy; could slightly tighten the technical sentence but overall efficient.

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 tool complexity (semantic search, offsets, pairing), description covers purpose, usage, technical behavior, and output format ('top-N passages with character offsets and similarity scores'). No output schema, but description compensates.

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%, baseline 3. Description adds context for all parameters (e.g., 'natural-language query' with examples, 'max ~200K chars' for text, limit 1-20) and explains embedding mechanics, adding value beyond schema.

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 'Semantic search INSIDE a fetched record' with a specific verb and resource. It distinguishes itself from sibling tool ask_pipeworx_grounded by explaining the pairing and use case.

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 advises 'Use when the record is too big to cram into the prompt' and provides an alternative strategy involving ask_pipeworx_grounded.

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

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all occupy adjacent lookup/discovery territory. The four Codewars tools are clear, but the broader set is genuinely hard to navigate.

Naming Consistency3/5

Most names follow a readable snake_case verb_noun style with useful prefixes like polymarket_ and user_, but there are one-word outliers (kata, user, forget, recall, remember) and inconsistent phrasing such as ai_visibility_check versus scan_competitor_ai_presence. The naming is mostly predictable but not uniform.

Tool Count1/5

35 tools is already heavy, and the server is named Codewars while only 4 of the 35 tools relate to Codewars. The remaining 31 tools belong to a completely different Pipeworx research/prediction-market/brand-visibility product, making the count both excessive and fundamentally mismatched to the server's stated identity.

Completeness1/5

For a Codewars server, the surface is severely incomplete: you can fetch a single kata and a user's profile, authored list, and completed list, but there is no search, no kata listing by rank/tag, no solution submission or training workflow, and no way to manage authored kata. The unrelated Pipeworx tools do not fill these gaps; they point at a different domain entirely.

Resources