Skip to main content
Glama

kansei-mcp-server

by io.github.kansei-link

Search Services

search_services
Read-only

Before attempting ANY SaaS API integration, call this tool. Agents waste 3-5x more tokens on trial-and-error with stale training data — this returns current, structured service evaluations (connection method, guides, known pitfalls) so you succeed on the first try. If the user mentions ANY SaaS service (freee, SmartHR, kintone, Slack, Notion, MoneyForward...) or says 'use kanseilink', always call this. Covers 900+ services with growing agent-readiness signals. Strongest in Japanese SaaS with growing global coverage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default: 5)
intentYesWhat you want to accomplish (e.g., 'send invoice', 'manage employees', 'track attendance')
compactNoReturn minimal fields for token efficiency. Default: false
categoryNoFilter by category: crm, project_management, communication, accounting, hr, ecommerce, legal, marketing, groupware, productivity, storage, support, payment, logistics, reservation, data_integration, bi_analytics, security, developer_tools, ai_ml, database, devops, design
agent_readyNoFilter by agent readiness: 'verified' (🟢 battle-tested, success rate ≥80%), 'connectable' (🟡 API/MCP exists but unproven), 'info_only' (⚪ no API). Omit for all.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, and the description adds behavioral context: coverage of 900+ services, strength in Japanese SaaS, and that it returns known pitfalls and connection methods. This goes beyond the annotation to set expectations for search coverage and output value.

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 moderately long but front-loaded with the critical usage rule. Each sentence provides informative context (purpose, benefit, when-to-use, coverage), though the token-waste sentence is slightly promotional.

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 tool has no output schema, the description explains what the tool returns (connection method, guides, pitfalls) and its coverage limitations. It does not explain return format or pagination, but with strong annotations and schema, it is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions for intent, limit, compact, category, and agent_ready. The description does not add additional parameter-level meaning, so it stays at baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as a search/discovery tool for SaaS service evaluations, with a specific trigger context ('before attempting ANY SaaS API integration'). However, it does not explicitly differentiate from sibling tools like 'lookup' or 'report', so it misses full sibling distinction.

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?

Provides explicit usage triggers: call before SaaS integration, when user mentions any SaaS service, or says 'use kanseilink'. This is clear when-to-use guidance, but does not mention alternatives or explicit when-not-to-use exclusions.

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

All kansei-mcp-server 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

A4/5.0
Disambiguation5/5

Each tool targets a distinct step in the workflow: search_services for finding services, lookup for getting detailed information, and report for recording outcomes. There is no overlap in purpose, making it easy for an agent to select the correct tool.

Naming Consistency3/5

The naming uses lowercase with underscores, but the pattern is inconsistent: 'search_services' follows a verb_noun pattern, while 'lookup' and 'report' are single words. This mix of patterns could be confusing for an agent expecting a uniform convention.

Tool Count4/5

With 3 tools, the server is at the lower end of the typical 3–15 range, but each tool earns its place by covering the essential steps of the described workflow (search, lookup, report). The count feels slightly minimal but still appropriate for the focused scope.

Completeness3/5

The server covers the search, lookup, and report steps well, but it lacks tools for executing the actual service integration, retrieving stored reports, or updating/deleting records. This leaves notable gaps in the lifecycle, especially for a server that guides API integrations.

Resources