SaaS intelligence for AI agents. 5 unified tools cover 1,000+ services with 91-96% token savings.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.1/5 across 3 of 3 tools scored.
Each tool targets a distinct stage in the workflow: search_services discovers services, lookup retrieves detailed information for a specific service, and report contributes feedback. No overlap exists, making selection unambiguous.
Names are all lowercase and imperative, but search_services uses a verb_noun pattern while lookup and report are single verbs. This is a minor deviation, and the names remain intuitive and predictable.
With 3 tools, the server is well-scoped for its purpose, covering the essential phases of the KanseiLink flow without unnecessary bloat. The count fits comfortably within the ideal range.
The tool set fully covers the described lifecycle: search_services for discovery, lookup for getting connection details, and report for contributing back to the community. No obvious gaps exist for the intended workflow.
lookupLookupAGet 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.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | No | Workflow goal — triggers recipe mode (e.g., 'onboard employee') | |
| mode | No | Explicit mode override | |
| detail | No | Get full connection guide (auth, endpoints, rate limits) | |
| period | No | Time period — triggers history mode | |
| service | No | Fuzzy service name — triggers combinations mode | |
| insights | No | Get aggregated usage data (success rate, trends, errors) | |
| services | No | Your available service IDs — for recipe coverage calculation | |
| service_id | No | Service ID (from search_services) | |
| compare_with | No | Competitor service_id for comparison — triggers history mode | |
| feedback_type | No | [feedback] Filter by feedback type | |
| feedback_limit | No | [feedback] Max results (default 20) | |
| feedback_status | No | [feedback] Filter by status. Triggers feedback mode when present. | |
| voice_agent_type | No | [voices] Filter by agent type (claude, gpt, gemini) | |
| voice_question_filter | No | [voices] Filter by question_id |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the description need not repeat safety info. It adds valuable behavioral context: default mode is tips, detail:true triggers full connection guide, insights:true returns usage data, goal triggers recipe mode. This goes beyond the annotations and schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core purpose, then mode options, then flow. Every sentence earns its place with no fluff. It is concise, well-structured, and easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main modes and the overall flow, which is sufficient for typical usage. It doesn't detail all special modes (feedback, voices, combinations, history), but the schema provides those details. Without an output schema, it doesn't explain return values, but for a lookup tool the return is implied. It's complete enough given the schema and annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds some context by referencing key parameters (detail, insights, goal) and their effects, but most parameter semantics are already fully documented in the schema. It doesn't compensate significantly for the 14 parameters, but it doesn't need to given the schema richness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get everything you need about a service before using it.' It distinguishes from sibling search_services by positioning as step 2 of the flow and listing distinct capabilities (tips, detail, insights, recipes). This is a specific verb+resource with clear scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides an explicit usage context: 'This is step 2 of the standard KanseiLink flow: search_services → lookup → (execute) → report.' This tells the agent when to use it relative to other tools. However, it does not explicitly state when not to use it or name alternative tools, so it's only a 4 rather than 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reportReportAInspectContribute data back to the KanseiLink community. Report success/failure after using a service (5 seconds, helps everyone), submit feedback, record API change events, or share your qualitative experience. PII is auto-masked. This is step 4 of the standard flow: search_services → lookup → (execute) → report.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | [feedback] Your feedback in detail. Write freely. | |
| mode | No | Explicit mode selection. Auto-detected from params if omitted: success → outcome, question_id → voice, event_type → event, subject+body → feedback. | |
| title | No | [event] Short event title (e.g., 'freee API v3 deprecation'). | |
| context | No | [outcome] Additional context about the usage (PII will be auto-masked). | |
| subject | No | [feedback] Short summary of your feedback (1 line). | |
| success | No | [outcome] Whether the operation succeeded. | |
| agent_id | No | Your agent identifier (optional, for follow-up). Used in feedback and voice modes. | |
| cost_usd | No | [outcome] Actual cost in USD (estimated from tokens if omitted). | |
| is_retry | No | [outcome] Whether this is a retry of a previously failed call. | |
| priority | No | [feedback] How important: low, normal, high, critical. Default: normal. | |
| recipe_id | No | [outcome] Recipe used for this attempt. | |
| task_type | No | [outcome] Operation performed (e.g., 'create_invoice', 'search_contacts'). | |
| agent_type | No | Agent platform type (claude, gpt, gemini, copilot, llama, deepseek, other). Used in outcome mode (auto-inferred from model_name if omitted) and voice mode. | |
| attempt_id | No | [outcome] attempt_id returned by lookup. | |
| confidence | No | [voice] How confident are you in this assessment? high, medium, low. | |
| error_type | No | [outcome] Error category if failed (e.g., 'auth_error', 'timeout', 'rate_limit', 'schema_mismatch'). | |
| event_date | No | [event] When the event occurred or takes effect (YYYY-MM-DD). | |
| event_type | No | [event] Category: api_change, api_deprecation, law_amendment, pricing_change, outage, security_incident, feature_launch, competitor_move, mcp_update, other. | |
| latency_ms | No | [outcome] Response time in milliseconds. | |
| model_name | No | [outcome] LLM model used (e.g., 'claude-sonnet-4', 'gpt-4o'). | |
| service_id | No | Service ID. Required for outcome and voice modes. Optional for feedback and event. | |
| workaround | No | [outcome] How you resolved the issue, if any. Helps future agents. | |
| description | No | [event] Details about the event and expected impact. | |
| failed_step | No | [outcome] Step identifier where execution stopped. | |
| question_id | No | [voice] Which question to answer: selection_criteria, would_recommend, biggest_frustration, best_feature, switching_likelihood, auth_experience, doc_quality, error_handling, compared_to_competitor, mcp_readiness, free_voice. | |
| input_tokens | No | [outcome] Input/prompt token count. | |
| feedback_type | No | [feedback] Type of feedback: suggestion, missing_data, correction, feature_request, workaround_tip, bug_report, praise, other. | |
| output_tokens | No | [outcome] Output/completion token count. | |
| response_text | No | [voice] Your honest answer in your own words. | |
| recipe_version | No | [outcome] Recipe version returned by lookup. | |
| estimated_users | No | [outcome] Approximate number of end-users your agent serves. | |
| impact_expected | No | [event] Expected impact: positive, negative, neutral, unknown. | |
| response_choice | No | [voice] Quick rating where applicable (e.g., 'strongly_yes', 'excellent', 'ready'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=false and idempotentHint=false; description does not contradict these. Adds valuable behavioral context: 'PII is auto-masked' and '5 seconds, helps everyone' characterize side effects and cost. Could disclose more (e.g., public community visibility), but adequate given annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with front-loaded purpose ('Contribute data back to the KanseiLink community') followed by concrete examples and flow context. No filler; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 33 parameters and 4 modes, the description provides sufficient high-level orientation: the standard flow, PII masking, and the four action types. Combined with the fully-described schema, an agent can understand when and how to invoke the tool. No output schema, but return values are not critical for a submission tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 33 parameters have rich schema descriptions with 100% coverage, so the description does not need to repeat them. It does not add parameter-specific semantics beyond the schema, which is acceptable per baseline. Description mentions the four modes but not how they map to parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verbs and resources: 'Report success/failure', 'submit feedback', 'record API change events', 'share your qualitative experience'. It clearly distinguishes from sibling tools by placing itself as step 4 in the standard flow: search_services → lookup → (execute) → report.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'This is step 4 of the standard flow', giving clear when-to-use context. Lists four distinct use cases. Does not explicitly state when-not-to-use or directly compare to alternatives, but the flow context makes usage boundaries clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_servicesSearch ServicesABefore 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.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (default: 5) | |
| intent | Yes | What you want to accomplish (e.g., 'send invoice', 'manage employees', 'track attendance') | |
| compact | No | Return minimal fields for token efficiency. Default: false | |
| category | No | Filter 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_ready | No | Filter by agent readiness: 'verified' (🟢 battle-tested, success rate ≥80%), 'connectable' (🟡 API/MCP exists but unproven), 'info_only' (⚪ no API). Omit for all. |
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.
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.
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.
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.
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.
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.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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