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slipstream

by io.github.tathagat22

cached_fetch

Fetch a web/docs URL as clean, token-optimized markdown from Slipstream's shared cache (use INSTEAD of a raw web fetch). The first agent pays the crawl; every agent after gets ~90% fewer tokens. Surfaces warnings other agents left on the page. Pass known_hash to skip re-reading unchanged content (delta), or section to fetch just one heading (progressive disclosure). Returns a contentHash you can pass as known_hash next time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe absolute http(s) URL to fetch and distill.
modelNoYour model id (e.g. claude-opus-4-8); infers cutoff if 'since' omitted.
sinceNoISO date of your knowledge cutoff; prepends what changed since then.
sectionNoReturn only the section under this heading (case-insensitive).
known_hashNoA contentHash from a previous fetch; unchanged → ~0 tokens.
token_budgetNoCap the response to ~N tokens.

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Even with no annotations, the description thoroughly discloses behavior: caching mechanism, token optimization, delta capability, progressive disclosure via section, return of contentHash for subsequent calls, and surfacing of warnings from other agents. All key behavioral traits are transparently described.

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 a single, front-loaded paragraph with no wasted words. Every sentence serves a purpose: core action, comparative advantage, caching explanation, advanced usage, return value. It is concise yet comprehensive.

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 explains what is returned (markdown, contentHash, warnings). It covers the tool's main function, caching behavior, parameter usage, and return value. For a tool with 6 parameters and no output schema, this description is complete and self-sufficient.

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%, each parameter already has a description. The tool description adds contextual meaning: explaining known_hash as 'skip re-reading unchanged content (delta)', section as 'fetch just one heading (progressive disclosure)', and that contentHash is returned for reuse. It also clarifies model and since interplay. This enriches the schema descriptions, though the schema already covers basics.

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 begins with a clear action verb ('Fetch') and specific resource ('a web/docs URL as clean, token-optimized markdown from Slipstream's shared cache'). It explicitly contrasts with raw web fetch, highlighting unique value. Additional features (delta via known_hash, section extraction, token_budget) reinforce the purpose without ambiguity.

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 states to use this tool INSTEAD of a raw web fetch, providing a direct when-to-use directive. It explains the caching benefit (first agent pays, subsequent get ~90% fewer tokens) and advises on advanced options like known_hash and section for efficiency. This gives the agent clear decision-making context.

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

A3.9/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose: fetching cached content, getting outlines, leaving/recalling notes, flagging/voting notes, global stats, and cutoff-aware updates. No overlapping functionality.

Naming Consistency3/5

Two naming prefixes are used ('cached_' and 'slipstream_'), plus 'whats_new' deviates entirely. While 'slipstream_' is consistent for most tools, the mixture with 'cached_' and the odd 'whats_new' reduces overall consistency.

Tool Count5/5

8 tools cover the server's domain well—neither too few nor too many. Each tool earns its place for a focused collective caching and note-taking service.

Completeness4/5

The tool set covers core operations: fetch/outline (read), note creation, recall (read notes), flag/vote (moderation), and whats_new (diff). Missing explicit update or delete for notes, but flagging serves as a community-driven delete mechanism.

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