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Read-only

Retrieve service tips, full connection guides, usage insights, and multi-service recipes. Use modes to access feedback, voice histories, or comparative data.

Instructions

Get everything you need about a service before using it. Default: tips (auth setup, pitfalls, workarounds). Add detail: true for full connection guide, insights: true for usage data. Pass goal: 'workflow description' to find multi-service recipes. This is step 2 of the standard KanseiLink flow: search_services → lookup → (execute) → report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
service_idNoService ID (from search_services)
goalNoWorkflow goal — triggers recipe mode (e.g., 'onboard employee')
servicesNoYour available service IDs — for recipe coverage calculation
serviceNoFuzzy service name — triggers combinations mode
periodNoTime period — triggers history mode
compare_withNoCompetitor service_id for comparison — triggers history mode
detailNoGet full connection guide (auth, endpoints, rate limits)
insightsNoGet aggregated usage data (success rate, trends, errors)
modeNoExplicit mode override
feedback_statusNo[feedback] Filter by status. Triggers feedback mode when present.
feedback_typeNo[feedback] Filter by feedback type
feedback_limitNo[feedback] Max results (default 20)
voice_question_filterNo[voices] Filter by question_id
voice_agent_typeNo[voices] Filter by agent type (claude, gpt, gemini)
Behavior5/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=false. The description adds behavioral traits by detailing mode triggering (e.g., service triggers combinations, period triggers history) and default output focus (tips on auth setup, pitfalls). 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 a single, dense paragraph with front-loaded purpose. It efficiently packs mode explanations and flow context, though breaking into bullets could improve readability.

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 (14 params, multiple modes, no output schema), the description covers usage well but omits details about return values or response structure, leaving some incompleteness.

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?

With 100% schema description coverage, baseline is 3. The description adds significant value by explaining parameter interactions and mode triggers (e.g., 'triggers recipe mode', 'triggers history mode'), going beyond mere listing.

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 'Get everything you need about a service before using it' and positions it as step 2 of a specific flow (search_services → lookup → execute → report), effectively distinguishing it from sibling tools like analyze, inspect, report, and search_services.

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 the default behavior (tips) and how to switch modes via parameters (detail, insights, goal, etc.), and places it in a workflow. It lacks explicit 'when not to use' guidance but is otherwise clear about usage 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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