Skip to main content
Glama

Crontab

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".

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, open-world, non-destructive. The description adds substantial context beyond this: embedding model (BGE-base-en), cosine similarity over 500-char windows, 200K char cap with truncation flagging, and character offsets for verbatim quote verification. No contradiction with annotations.

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 dense sentences, front-loaded with the core purpose. Every sentence adds value: usage context, pairing with sibling, technical details. No fluff.

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?

Despite lacking an output schema, the description fully explains the return format (top-N passages with offsets and scores), parameter roles, input limits and truncation behavior, and integration with a key sibling tool. This is complete for a search tool.

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%, so baseline is 3. The description adds semantic meaning beyond the schema: 'text' is 'text you already pulled', 'query' is a natural-language query with examples, and 'get back the top-N passages' clarifies how 'limit' behaves. This elevates it above the baseline.

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 a specific verb+resource: 'Semantic search INSIDE a fetched record.' It distinguishes itself from siblings by explicitly naming ask_pipeworx_grounded and describing its complementary role. The examples (SEC 10-K, article) make the purpose tangible.

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 'Use when...' guidance: when the record is too big to cram into the prompt. It also explains how it pairs with ask_pipeworx_grounded, giving clear context for when to choose this tool over alternatives like fetching the whole document.

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

All Crontab 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

A3.7/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer factual questions with subtle differences that are not immediately clear. Additionally, five polymarket tools cover similar ground (arbitrage, edges, fill risk, spread), making it hard to pick the right one without reading the full descriptions.

Naming Consistency4/5

Tool names are consistently snake_case and mostly follow a verb_noun pattern (e.g., describe_cron, next_runs, validate_claim). Minor deviations exist (bet_research, entity_profile, pipeworx_trending) but the overall style is predictable and readable.

Tool Count2/5

33 tools is well over the high end for a focused server, and the server name 'Crontab' implies a narrow cron utility while most tools are a broad data-research platform. This mismatch makes the count feel bloated and poorly scoped.

Completeness2/5

For a cron server, the set is severely incomplete: only describe_cron and next_runs exist, with no create/delete/update functionality. For the actual data-research domain, it is rich but lacks clear CRUD coverage for many resources, and the inclusion of unrelated cron/memory tools creates dead ends.

Resources