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sumo_qa_load_skill_context

Read-onlyIdempotent

Load a specific slice of a skill's context — manifest, section, module, or full body — as JSON. Check for changes using a known hash to skip re-sending unchanged content.

Instructions

Load just one slice of a skill's context as a JSON string, instead of the whole SKILL.md body.

mode:

  • "manifest" — routing summary + section list + module list;

  • "section" — one section's text (pass section, an id from the manifest);

  • "module" — one module's text (pass module, an id from the manifest);

  • "full" — the entire SKILL.md body, byte-for-byte identical to the existing zero-argument skill tool for skill_name; a body over the host's per-response token cap is returned as an oversize pointer to the manifest/section/module slices instead of failing (#393).

The section/module/full slices each return content_hash (sha256 of the returned text) and estimated_tokens. Pass known_hash to ask "has this slice changed since hash X?": a match returns changed=false with the body omitted (saving the re-send), a mismatch returns changed=true with the body. This is derived per call — there is NO hidden session cache, so it is safe across hosts regardless of MCP session identity.

Never raises: an unknown skill_name/mode/section/module, a missing required arg, or a path-traversal attempt returns a JSON error envelope listing the valid choices. Read-only and local-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
moduleNo
sectionNo
known_hashNo
skill_nameNo
Behavior5/5

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

Despite annotations already indicating readOnlyHint=true, idempotentHint=true, the description adds significant value: it explains the known_hash change detection mechanism, error handling (never raises, returns JSON error envelope), and guarantees about being local-only and safe across hosts. No contradictions.

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?

The description is well-structured with bullet points for modes and clear separation of concepts. It is slightly lengthy but every sentence adds value, and it is front-loaded with the core purpose.

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?

Given the complexity (5 parameters, no output schema), the description is thorough: it covers modes, parameter usage, error handling, change detection, and safety. It does not formally describe the response structure but mentions key fields like content_hash and estimated_tokens, which is sufficient for an AI agent.

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 description coverage is 0%, but the description fully compensates by detailing the allowed values for mode (manifest, section, module, full) and how section and module parameters correspond to manifest IDs. It also explains known_hash usage. All 5 parameters are meaningfully described.

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 tool loads a slice of a skill's context as a JSON string, explicitly listing modes (manifest, section, module, full). It distinguishes this from loading the whole SKILL.md body and from sibling tools like sumo_qa_load_approaches.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

The description explains when to use each mode, especially noting that 'full' mode is identical to the existing zero-argument skill tool and that oversize bodies return a pointer to slices. It provides clear context for when to use this tool over alternatives.

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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