Reviewed public-data search and execution with provenance and verifiable integrity receipts.
Run one allowlisted, bounded HTTPS GET contract and return extracted values, provenance, and an integrity receipt.
| Name | Required | Description | Default |
|---|---|---|---|
| recipeId | Yes | ||
| parameters | No |
| Name | Required | Description |
|---|---|---|
| status | Yes | |
| receipt | Yes | |
| results | Yes | |
| question | Yes | |
| recipe_id | Yes | |
| elapsed_ms | Yes | |
| parameters | Yes | |
| provenance | Yes | |
| request_url | Yes | |
| retrieved_at | Yes | |
| interpretation | Yes | |
| contract_version | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds that it is a bounded HTTPS GET and returns specific items, providing some context beyond annotations. However, it does not disclose auth requirements, rate limits, or potential side effects (though none expected).
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?
Single sentence with efficient wording, no redundancy. All necessary high-level information is front-loaded without extraneous details.
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 presence of an output schema and a single required parameter, the description is partially complete. However, it omits explanation of the optional parameters object and does not describe the output in detail. The existing output schema partially compensates, but the lack of parameter semantics is a notable gap.
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 0%, yet the description provides no explanation of the two parameters (recipeId and parameters). The meaning of recipeId is somewhat inferable from the tool's name and title, but the parameters object and its constraints are unexplained. The output schema exists but is separate; this is a gap.
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 clearly states the action (execute), resource (retrieval contract), constraints (allowlisted, bounded, HTTPS GET), and outputs (extracted values, provenance, integrity receipt). It distinguishes from siblings by describing a specific execution action as opposed to search or verify.
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?
Description implies usage for executing a contract but does not provide when-to-use or when-not-to-use guidance relative to siblings search_data and verify. No explicit exclusions or alternative selection criteria are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search current metadata from e-Gov Data Portal and Statistics Dashboard, then return a short ranked list with publisher, formats, coverage, usage terms, and official links.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | Search all sources, downloadable datasets, or statistical series | all |
| limit | No | ||
| query | Yes | What data to find, preferably in Japanese |
| Name | Required | Description |
|---|---|---|
| note | Yes | |
| query | Yes | |
| total | Yes | |
| status | Yes | |
| results | Yes | |
| sources | Yes | |
| available | Yes | |
| searched_at | Yes | |
| interpreted_as | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare it read-only, open-world, and idempotent. The description adds value by specifying data sources ('current metadata from e-Gov Data Portal and Statistics Dashboard') and the output format ('short ranked list with publisher, formats, coverage, usage terms, and official links'), which goes beyond annotation hints.
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?
Single sentence, front-loaded with the primary action, and every phrase adds specific information (sources, output characteristics). No filler or redundancy.
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?
With a read-only search tool and an existing output schema, the description adequately covers functionality and return fields. However, it omits any mention of result limitations, no-results behavior, or default ranking, which would make it more self-contained.
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 only 67% (query and kind described, limit missing). The description does not compensate by explaining parameter usage or the limit parameter, nor does it clarify search language preferences beyond what the schema already provides.
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 uses a specific verb ('Search') and identifies the exact resources (e-Gov Data Portal and Statistics Dashboard) and output structure (ranked list with publisher, formats, coverage, usage terms, links). This clearly distinguishes it from sibling tools like 'execute' and 'verify'.
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?
No guidance is provided on when to choose this tool over others. The description does not mention alternatives, exclusions, or use-case context despite having sibling tools (execute, verify) in the same namespace.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check receipt and result hashes plus the execution envelope against the reviewed catalog. This detects later modification; it does not prove publisher authenticity or current freshness.
| Name | Required | Description | Default |
|---|---|---|---|
| execution | Yes |
| Name | Required | Description |
|---|---|---|
| valid | Yes | |
| checks | Yes | |
| receipt_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations by clarifying that the tool detects modification but does not verify authenticity or freshness. Annotations already indicate readOnly, idempotent, non-destructive, and no contradiction is present.
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?
Two concise sentences with no redundant information, front-loaded with the action and followed by important limitations. Every sentence adds value.
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 presence of an output schema (not shown), the description does not need to explain return values. It covers purpose, limitations, and input semantics adequately, though more detail on the input object structure would improve completeness.
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?
With 0% schema description coverage, the description partially compensates by mentioning 'receipt and result hashes plus the execution envelope', but the input object structure is not fully explained. Baseline for single parameter with low coverage is 3.
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 title 'Verify an execution receipt' and description 'Check receipt and result hashes plus the execution envelope against the reviewed catalog' provide a specific verb and resource, clearly distinguishing it from sibling tools 'execute' and 'search_data'.
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 explicitly states what the tool does not do ('does not prove publisher authenticity or current freshness') and its purpose (detect later modification), guiding the agent on when to use it. However, it does not explicitly mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
search_data2 fields changedOutput schema / properties / interpreted_asAdded value: +{
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Output schema / requiredPrevious value: -[
- "status",
- "query",
- "searched_at",
- "results",
- "total",
- "available",
- "sources",
- "note"
-]New value: +[
+ "status",
+ "query",
+ "interpreted_as",
+ "searched_at",
+ "results",
+ "total",
+ "available",
+ "sources",
+ "note"
+]search_data18 fields changedInput schema / properties / domainRemoved value: -{
- "maxLength": 100,
- "minLength": 1,
- "type": "string"
-}Input schema / properties / kindAdded value: +{
+ "default": "all",
+ "description": "Search all sources, downloadable datasets, or statistical series",
+ "enum": [
+ "all",
+ "dataset",
+ "statistics"
+ ],
+ "type": "string"
+}Input schema / properties / limit / maximumPrevious value: -50New value: +20Input schema / properties / query / descriptionPrevious value: -"Words in Japanese or English"New value: +"What data to find, preferably in Japanese"Input schema / properties / query / maxLengthPrevious value: -200New value: +80Input schema / properties / query / minLengthPrevious value: -1New value: +2Input schema / properties / sourceIdRemoved value: -{
- "maxLength": 100,
- "minLength": 1,
- "type": "string"
-}Input schema / requiredAdded value: +[
+ "query"
+]Output schema / properties / availableAdded value: +{
+ "additionalProperties": {},
+ "propertyNames": {
+ "type": "string"
+ },
+ "type": "object"
+}Output schema / properties / catalog_versionRemoved value: -{
- "type": "string"
-}Output schema / properties / matchesRemoved value: -{
- "items": {},
- "type": "array"
-}Output schema / properties / noteAdded value: +{
+ "type": "string"
+}Output schema / properties / queryAdded value: +{
+ "type": "string"
+}Output schema / properties / resultsAdded value: +{
+ "items": {},
+ "type": "array"
+}Output schema / properties / searched_atAdded value: +{
+ "type": "string"
+}Output schema / properties / sourcesAdded value: +{
+ "items": {},
+ "type": "array"
+}Output schema / properties / statusAdded value: +{
+ "const": "ok",
+ "type": "string"
+}Output schema / requiredPrevious value: -[
- "matches",
- "total",
- "catalog_version"
-]New value: +[
+ "status",
+ "query",
+ "searched_at",
+ "results",
+ "total",
+ "available",
+ "sources",
+ "note"
+]executesearch_dataverifyClaiming 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, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
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.
No comments yet. Be the first to start the discussion!
Read bounded illicit-economy evidence with provenance, privacy, availability, and federation intact.
Cryptographically anchored evidence for agents: verified run receipts, proof-gated settlement.
Tamper-evident daily ledgers of 12 Japanese public-data domains, with cross-ledger entity search.
Evidence-bounded static agent security checks with signed redacted receipts.
Add one secure layer between your agents and this server.
Each tool targets a distinct concern: execute retrieves data via a contract, search_data queries metadata, and verify checks integrity receipts. No overlap or ambiguity between them.
All tool names are lowercase snake_case with a verb-first style (execute, search_data, verify). While not strictly verb_noun, the naming is predictable and consistent, though a less uniform pattern than the calibration example.
Three tools is a well-scoped set for a public data catalog: search, retrieve, and verify. Each tool serves a necessary role with no bloat or missing core functionality.
The domain covers discovering, accessing, and validating public data. search_data finds datasets, execute retrieves them, and verify ensures integrity—covering the full workflow without obvious gaps.
