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kks32

designsafe-mcp

by kks32

fetch_corpus

Fetch grounding sources directly from their GitHub repos into the corpus, for deployments without local checkouts. Supports fetching a specific source or all sources; reindex afterward.

Instructions

Fetch grounding sources live from their canonical GitHub repos into corpus/, for deployments without local checkouts.

source: fetch just one logical source by name; empty fetches every GitHub-backed source. CommunityData is not fetched here (needs Tapis auth; see corpus_status for the command). Run reindex after.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool writes to corpus/, excludes CommunityData due to Tapis auth requirements, and requires a reindex afterward. However, it does not explicitly state whether it overwrites existing files, requires network/GitHub access, or any failure modes, leaving some behavioral traits implicit.

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 three sentences that each add value: purpose/context, parameter explanation, and exclusion/next-step. It is front-loaded with the main verb and resource, with no filler or redundant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with an output schema, the description covers purpose, parameter usage, exclusions, and post-conditions (reindex). It omits only niche details like the list of valid source names or explicit network requirements, but is otherwise sufficient for an agent to select and invoke the tool correctly.

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?

The schema provides only a name 'source' with no description, but the description compensates by explaining the parameter semantics: 'fetch just one logical source by name; empty fetches every GitHub-backed source.' This clarifies the default behavior and scope of the parameter, though it doesn't enumerate valid source names.

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 action ('Fetch grounding sources live from their canonical GitHub repos into corpus/') and the context ('for deployments without local checkouts'). It also differentiates from sibling tools by noting what it does not handle (CommunityData) and the follow-up action (reindex).

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 gives explicit when-to-use context ('for deployments without local checkouts') and an explicit exclusion ('CommunityData is not fetched here; needs Tapis auth; see corpus_status'). It also provides next-step guidance ('Run reindex after'), covering both alternatives and follow-ups.

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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