SaaS intelligence for AI agents. 5 unified tools cover 1,000+ services with 91-96% token savings.
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.
| 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.
Record what happened after using a service: success/failure outcome, feedback, API change events, or qualitative experience. Data is saved to this installation's LOCAL database only (improves local recovery hints and stats) — nothing is sent to KanseiLink unless you separately opt in to sharing. PII is auto-masked before storage. 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, so the tool is expected to have side effects. The description explicitly states the data is saved locally and nothing is sent to an external service unless opted in, which goes beyond the annotations to clarify the mutation scope. It also mentions PII auto-masking, a behavioral detail not captured by annotations. However, it does not describe the return value or failure behavior (e.g., what happens on duplicate submission), which would further improve transparency. With annotations already hinting at non-read and non-idempotent behavior, the description adds meaningful context but is not fully comprehensive.
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 extremely efficient. The first sentence declares the core purpose and data categories. The second sentence adds crucial privacy and storage context. The third sentence positions the tool in the overall workflow. No wasted words; every sentence adds distinct value. The structure is front-loaded with the most actionable information.
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's complexity (33 parameters, 0 required, 4 modes, no output schema), the description is remarkably complete. It covers purpose, usage flow, privacy, data locality, and parameter auto-detection. Since there is no output schema, the description doesn't need to describe return values, but it could benefit from a brief note about the response format. Nonetheless, for a reporting tool with rich schema annotations, this description provides sufficient context for an agent to use it correctly.
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%, meaning every parameter has a schema-level description. The tool description adds value by explaining the auto-detection logic for the 'mode' parameter ('Auto-detected from params if omitted: success → outcome, question_id → voice, event_type → event, subject+body → feedback') and groups parameters by mode (e.g., '[outcome]', '[feedback]', '[voice]', '[event]') directly in their schema descriptions. This helps the agent understand which parameters are relevant for each mode. The description could be stronger by directly linking the modes to use cases, but the schema itself is already rich.
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 begins with 'Record what happened after using a service: success/failure outcome, feedback, API change events, or qualitative experience.' This provides a very specific verb ('Record'), a clear resource ('what happened after using a service'), and enumerates the distinct data categories (outcome, feedback, event, voice). It also distinguishes this tool from its siblings by explicitly stating it is 'step 4 of 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?
The description provides explicit when-to-use guidance: 'This is step 4 of the standard flow: search_services → lookup → (execute) → report.' This clearly positions the tool in the sequence relative to its siblings (lookup and search_services). Additionally, it describes the data storage behavior ('Data is saved to this installation's LOCAL database only… nothing is sent to KanseiLink unless you separately opt in to sharing') and privacy handling ('PII is auto-masked before storage'), giving the agent important context for when to invoke this tool and its privacy implications.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| 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.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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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.
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.
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.
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.
