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Glama

Chargebee

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

A5/5.0
Behavior5/5

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

Describes technical details beyond annotations: uses BGE-base-en embeddings, cosine similarity over 500-char overlapping windows, 200K character cap with truncation flag, and output includes character offsets. Annotations already convey read-only and idempotent nature; description adds valuable implementation context.

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?

Efficiently front-loaded: first sentence states core purpose and key features. Uses bold for emphasis. Every sentence earns its place—no fluff, while covering algorithm, limits, and pairing guidance.

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?

Completes the tool's context: explains input limits (200K chars), algorithm (BGE embeddings, cosine), output format (passages with offsets and scores), and relationship to sibling tool. Despite no output schema, it adequately describes return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage, but description enriches parameters with examples (e.g., query examples like 'supply-chain risk'), constraints (200K char limit for text, limit range 1-20), and explains how text is processed (truncation flag). Adds significant value beyond schema alone.

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?

Clearly states it performs semantic search inside a fetched record, with specific examples like SEC 10-K and articles. It distinguishes from sibling tools by specifying when to use (record too large for prompt) and mentions 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance: 'Use when the record is too big to cram into the prompt'. Mentions alternative workflow with ask_pipeworx_grounded, giving clear context for agent decision-making.

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

B3.3/5.0
Disambiguation1/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all answer questions or discover data via the same routing engine, with ask_pipeworx_beta explicitly stated to be identical to ask_pipeworx. Polymarket tools (arbitrage, edges, edge_tracker, fill_risk) also blur together, and ai_visibility_check overlaps with scan_competitor_ai_presence.

Naming Consistency2/5

Some clusters are consistent (chargebee_list_*/chargebee_get_*, polymarket_*, pipeworx_*), but the set mixes snake_case with varying verb styles and many unprefixed tools (remember, recall, forget, subscribe, unsubscribe, validate_claim). The 5 Chargebee tools use a clean prefix while the other 31 tools follow several different conventions, making the overall pattern unpredictable.

Tool Count2/5

36 tools is excessively heavy for a server named Chargebee, especially since only 5 tools actually relate to Chargebee. The remaining 31 tools form a broad Pipeworx/prediction-market/utility toolkit that has little connection to the server's apparent billing purpose, making the count feel bloated and unfocused.

Completeness1/5

The Chargebee-specific surface is severely incomplete: it only supports reading customers, subscriptions, and invoices, with no create, update, delete, payment, dunning, coupon, or plan-management operations. The rest of the tools belong to unrelated domains, so the set as a whole has no coherent lifecycle coverage and would leave agents unable to perform even basic Chargebee management tasks.

Resources