Spain's contributions to world science, exploration and culture, rigorously sourced
Answer a question from the corpus. Returns the matched object's claims with sources and confidence — never an unsourced answer. Use this when the user asked a question in words; use search when you want to see the candidates yourself.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | The question to answer, in the words the user asked it. |
| Name | Required | Description |
|---|---|---|
| claims | No | |
| entity | No | |
| answered | Yes | |
| matched_question | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even though annotations already declare readOnlyHint, idempotentHint, and destructiveHint/false, the description adds meaningful behavioral context beyond annotations: it returns claims with sources and confidence, and it never returns an unsourced answer. This clarifies the tool's output policy and constraints, which is valuable for an agent deciding whether the result is trustworthy.
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 two sentences with no filler. The primary behavior is front-loaded, the output guarantee is concise, and the usage contrast with search is efficient. 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?
For a one-parameter tool with a full output schema and safety annotations, the description covers what the tool does, when to use it, what it returns, and how it differs from the main alternative. Nothing critical is missing for correct invocation by an agent.
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 coverage is 100% and the single parameter is already described in the input schema as "The question to answer, in the words the user asked it." The tool description reinforces the verbatim aspect but does not add substantial new meaning beyond the schema, so baseline 3 is appropriate.
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 states a specific verb and resource: "Answer a question from the corpus" and clearly describes the output as matched claims with sources and confidence. It also distinguishes itself from the sibling tool search by naming the exact contrast, so an agent can tell them apart without opening other schemas.
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?
Explicit usage guidance is provided: "Use this when the user asked a question in words; use search when you want to see the candidates yourself." This gives both a positive condition and an explicit alternative, leaving no ambiguity about when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch one knowledge object by id, with its claims and the sources each claim cites. Use this once search, answer or get_topic has given you an id. An unknown id is not a dead end: the answer names near-miss ids you can retry with.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The knowledge object's id, as returned by search, answer, get_topic or get_related — the last segment of its canonical URL. |
| Name | Required | Description |
|---|---|---|
| found | Yes | |
| entity | No | |
| recovery | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond that: it states the return payload includes claims and sources, and it discloses the behavior for unknown ids (not a dead end, with retry suggestions from the answer). This gives the agent important expectations not captured by 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?
The description is two sentences with the primary action and return content front-loaded, followed by usage timing and error behavior. Every sentence carries distinct, necessary information with no redundancy or fluff.
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 only one parameter, full schema coverage, an output schema present, and annotations covering the safety profile, the description supplies all essential context: what the tool returns, when to invoke it, and how to handle unknown ids. Nothing an agent needs to call it correctly is missing.
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?
The schema description for 'id' has 100% coverage and already explains the id's provenance and format ('as returned by search, answer, get_topic or get_related — the last segment of its canonical URL'). The description reinforces that the id comes from search, answer, or get_topic, but adds no new meaning beyond the schema, so the baseline of 3 is appropriate.
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 ('Fetch') and a specific resource ('one knowledge object by id'), and further details that the result includes claims and the sources each claim cites. This clearly differentiates it from sibling tools like get_topic or get_sources, which likely fetch different resources or just sources.
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 when to use the tool: 'Use this once search, answer or get_topic has given you an id.' It also provides error-recovery guidance for unknown ids, explaining that the answer names near-miss ids to retry. This is an explicit when-to-use instruction that leaves little to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most recently verified knowledge objects (freshness signal). Use this to judge how current the corpus is, or to see what changed since you last read it. It ranks by verification date and ignores your topic entirely — use search or get_topic when you want objects that are relevant rather than recent.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many recently verified objects to return, newest verification first. |
| Name | Required | Description |
|---|---|---|
| total | Yes | |
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as readOnly, idempotent, and non-destructive, so the description does not need to repeat safety. It adds behavioral context beyond annotations: results are ordered by verification date, are topic-independent, and serve as a freshness signal. It does not contradict any annotation and clarifies ranking semantics.
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 two sentences with zero filler. The first sentence states what the tool returns and labels the freshness signal; the second provides use cases and alternative routing. This is front-loaded and economical.
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?
For a one-parameter, read-only tool with an output schema and full param documentation, the description covers purpose, usage, and differentiation. The only implicit context, 'Hispanic Legacy' from the title, does not weaken an agent's ability to invoke the tool 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%, with the limit parameter fully explained in the input schema ('How many recently verified objects to return, newest verification first'). The description adds no extra parameter detail, but none is needed; per the rubric, high schema coverage sets a baseline of 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 description opens with 'Most recently verified knowledge objects' and explicitly states it ranks by verification date, naming the resource (verified objects) and the operation (return most recent). It distinguishes itself from search and get_topic by noting it ignores topic relevance, making its scope unambiguous relative to siblings.
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 gives explicit use cases: 'judge how current the corpus is' and 'see what changed since you last read it.' It also provides a clear exclusion rule: 'ignores your topic entirely — use search or get_topic when you want objects that are relevant rather than recent,' so an agent knows exactly when not to select this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Corpus overview: what this instance knows, counts by type, published tags, freshness. Use this first when you land here and do not yet know whether this corpus can answer your question.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| tags | Yes | |
| by_type | Yes | |
| instance | Yes | |
| description | Yes | |
| total_media | Yes | |
| total_objects | Yes | |
| newest_verification | Yes | |
| oldest_verification | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description does not need to restate those. It adds context about returned data (counts, tags, freshness) but does not disclose additional behavioral traits such as authentication or rate limits, which is acceptable given the 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?
The description is compact and front-loaded: it opens with the core purpose, then lists contents, then gives a clear usage directive. Every sentence contributes value with no 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?
For a zero-parameter tool with full annotations and an output schema, the description is complete. It tells the agent what the tool returns, when to use it, and how it fits into the workflow. Nothing essential is missing.
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?
The tool has zero parameters and 100% schema description coverage, so there is no parameter burden on the description. The baseline of 4 applies because no parameter documentation is needed.
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 states a clear verb-like resource ('Corpus overview') and specifies what it contains: 'what this instance knows, counts by type, published tags, freshness.' It clearly distinguishes itself from siblings by positioning itself as the initial orientation tool.
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 guidance: 'Use this first when you land here and do not yet know whether this corpus can answer your question.' This gives clear context for when to use the tool, though it does not explicitly name alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The instance's whole source registry, or just the sources cited by one object — each with its evidence tier, reliability and access date. Use this when you need the registry entry behind a citation, or the whole registry to judge the corpus before trusting it; get_entity already tells you which sources a claim cites.
| Name | Required | Description | Default |
|---|---|---|---|
| object_id | No | Restrict the answer to the sources cited by this knowledge object. Omit it to get the instance's whole source registry. |
| Name | Required | Description |
|---|---|---|
| found | Yes | |
| sources | Yes | |
| recovery | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to restate safety. It adds useful behavioral context by specifying the two retrieval modes (whole registry vs. object-scoped) and the included fields (evidence tier, reliability, access date). This goes beyond the structured annotations without contradicting them.
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 two sentences with no wasted words. It front-loads the core behavior, then immediately gives usage guidance and a sibling-tool distinction. Every clause 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?
Given the single optional param, rich annotations, and presence of an output schema, the description covers what the tool does, when to use it, how to toggle scope, and how it relates to get_entity. Nothing essential is missing for an agent to select and invoke 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?
The input schema already provides 100% parameter description coverage, including the meaning of object_id and the omit-to-get-whole-registry behavior. The description mirrors this guidance rather than adding new parameter semantics. A baseline of 3 is appropriate because the schema carries the load.
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 returns the instance's whole source registry or sources cited by one object, each with evidence tier, reliability, and access date. It distinguishes itself from get_entity by explicitly noting that get_entity already tells which sources a claim cites. The verb 'get' plus resource 'sources' makes the scope precise.
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 tells when to use this tool: when needing the registry entry behind a citation or the whole registry to judge the corpus. It also provides an alternative, get_entity, and implies when not to use get_sources, since get_entity already provides which sources a claim cites. This is strong practical routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the knowledge objects carrying a tag (topics are content-backed tags). Use this to browse a known topic; use search when you have a question rather than a tag, and get_overview to see which tags exist. An unknown tag comes back with the topics that do exist, so a miss still moves you forward.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | Yes | The topic to list, spelled exactly as get_overview publishes it. Every published topic has objects under it, so an empty answer means the topic does not exist. |
| Name | Required | Description |
|---|---|---|
| tag | Yes | |
| total | Yes | |
| results | Yes | |
| recovery | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond annotations: unknown tags return the topics that do exist, so a miss still yields useful information. This clarifies an edge-case behavior that annotations cannot express.
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 compact and front-loaded, with every sentence contributing either core behavior, routing guidance, or an important edge case. No filler or redundant restatement of the schema exists.
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?
For a single-parameter read-only tool with a full output schema and rich annotations, the description covers the necessary context: what the tool does, when to use it, how to treat unknown tags, and how parameter spelling is validated. Nothing essential is missing.
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 coverage is 100% and the tag parameter is already documented. The description adds extra semantic value by stating the tag must be spelled exactly as get_overview publishes it and that an empty answer means the topic does not exist, which helps agents avoid false assumptions.
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 opens with a specific verb and resource: 'List the knowledge objects carrying a tag.' It distinguishes itself from search and get_overview, and clarifies that topics are content-backed tags, so an agent can tell exactly what this tool returns and how it differs from sibling tools.
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?
It explicitly states when to use this tool ('browse a known topic'), when to use search ('when you have a question rather than a tag'), and when to use get_overview ('to see which tags exist'). It even explains the behavior for an unknown tag, giving clear decision guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full-text search over the knowledge graph. Matching ignores accents and apostrophes, so query in the user's own words; every hit carries the fields it matched and a score. Text match, weighted by where the token hits (name 3, tags 2, content 1 per query token), plus a centrality bonus of up to 0.9 for objects that other objects point at. That bonus is why two hits can share matched_fields and still score differently: the decimal is inbound edges, not text relevance. Use this whenever you have a question rather than an id, then follow up with get_entity.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many ranked hits to return, best first. | |
| query | Yes | What to look for, in the user's own words. Matched against titles, questions, claim text and tags; accents and apostrophes are ignored, so 'jose' finds 'José'. |
| Name | Required | Description |
|---|---|---|
| total | Yes | |
| results | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent/destructive annotations, the description reveals how matching normalizes accents and apostrophes, the exact token weights per field, and the centrality bonus mechanics. It even warns that the decimal represents inbound edges, not text relevance, which prevents misinterpretation of scores.
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 front-loaded with the core purpose, then efficiently layers normalization, scoring, and usage guidance. Every sentence contributes distinct information, and the whole thing remains compact enough for quick reading.
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 output schema and annotations covering safety, the description fully covers what the agent needs: when to use it, how matching works, how scores are computed, and what the follow-up should be. Nothing essential is missing for a search tool of this complexity.
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 schema already documents query and limit. The description mostly repeats the normalization behavior from the query parameter and adds scoring context, but it does not introduce new parameter meaning beyond what the schema provides. Baseline 3 is appropriate.
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 opens with a precise verb and resource: 'Full-text search over the knowledge graph.' It also distinguishes itself from siblings by stating this is for questions rather than IDs and explicitly names get_entity as the follow-up tool, so an agent can select it correctly.
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?
'Use this whenever you have a question rather than an id, then follow up with get_entity' provides a clear decision rule and names an alternative. This is explicit enough for an agent to know when search is appropriate versus when an entity lookup is needed.
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.
answer1 field changedInput schema / properties / question / descriptionAdded value: +"The question to answer, in the words the user asked it."get_entity3 fields changedInput schema / properties / id / descriptionAdded value: +"The knowledge object's id, as returned by search, answer, get_topic or get_related — the last segment of its canonical URL."Output schema / properties / entity / properties / mediaAdded value: +{
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "alt": {
+ "type": "string"
+ },
+ "caption": {
+ "type": "string"
+ },
+ "credit": {
+ "type": "string"
+ },
+ "height": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ "id": {
+ "type": "string"
+ },
+ "license_spdx": {
+ "type": "string"
+ },
+ "permissions": {
+ "additionalProperties": {
+ "type": "boolean"
+ },
+ "propertyNames": {
+ "type": "string"
+ },
+ "type": "object"
+ },
+ "supports": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ },
+ "width": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ }
+ },
+ "required": [
+ "id",
+ "url",
+ "alt",
+ "credit",
+ "license_spdx",
+ "permissions"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+}Output schema / properties / entity / requiredPrevious value: -[
- "id",
- "type",
- "name",
- "summary",
- "tags",
- "evidence_tier",
- "confidence",
- "last_verified",
- "canonical_url",
- "api_url",
- "questions",
- "claims"
-]New value: +[
+ "id",
+ "type",
+ "name",
+ "summary",
+ "tags",
+ "evidence_tier",
+ "confidence",
+ "last_verified",
+ "canonical_url",
+ "api_url",
+ "questions",
+ "claims",
+ "media"
+]get_latest1 field changedInput schema / properties / limit / descriptionAdded value: +"How many recently verified objects to return, newest verification first."get_overview2 fields changedOutput schema / properties / total_mediaAdded value: +{
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+}Output schema / requiredPrevious value: -[
- "instance",
- "description",
- "total_objects",
- "by_type",
- "tags",
- "newest_verification",
- "oldest_verification"
-]New value: +[
+ "instance",
+ "description",
+ "total_objects",
+ "by_type",
+ "tags",
+ "newest_verification",
+ "oldest_verification",
+ "total_media"
+]get_related1 field changedInput schema / properties / id / descriptionAdded value: +"The knowledge object to walk out from, by id."get_sources1 field changedInput schema / properties / object_id / descriptionAdded value: +"Restrict the answer to the sources cited by this knowledge object. Omit it to get the instance's whole source registry."get_topic1 field changedInput schema / properties / tag / descriptionAdded value: +"The topic to list, spelled exactly as get_overview publishes it. Every published topic has objects under it, so an empty answer means the topic does not exist."search4 fields changedInput schema / properties / limit / descriptionAdded value: +"How many ranked hits to return, best first."Input schema / properties / query / descriptionAdded value: +"What to look for, in the user's own words. Matched against titles, questions, claim text and tags; accents and apostrophes are ignored, so 'jose' finds 'José'."Output schema / properties / results / items / properties / matched_fields / descriptionAdded value: +"Which fields the query hit: name, tags, content. Does NOT determine the score on its own."Output schema / properties / results / items / properties / score / descriptionAdded value: +"Text match, weighted by where the token hits (name 3, tags 2, content 1 per query token), plus a centrality bonus of up to 0.9 for objects that other objects point at. That bonus is why two hits can share matched_fields and still score differently: the decimal is inbound edges, not text relevance."get_entity1 field changedOutput schema / properties / recoveryAdded value: +{
+ "additionalProperties": false,
+ "properties": {
+ "available": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "available_count": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ "available_truncated": {
+ "type": "boolean"
+ },
+ "did_you_mean": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "hint": {
+ "type": "string"
+ },
+ "next_tool": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "available_count",
+ "available",
+ "next_tool",
+ "hint"
+ ],
+ "type": "object"
+}get_related1 field changedOutput schema / properties / recoveryAdded value: +{
+ "additionalProperties": false,
+ "properties": {
+ "available": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "available_count": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ "available_truncated": {
+ "type": "boolean"
+ },
+ "did_you_mean": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "hint": {
+ "type": "string"
+ },
+ "next_tool": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "available_count",
+ "available",
+ "next_tool",
+ "hint"
+ ],
+ "type": "object"
+}get_sources1 field changedOutput schema / properties / recoveryAdded value: +{
+ "additionalProperties": false,
+ "properties": {
+ "available": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "available_count": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ "available_truncated": {
+ "type": "boolean"
+ },
+ "did_you_mean": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "hint": {
+ "type": "string"
+ },
+ "next_tool": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "available_count",
+ "available",
+ "next_tool",
+ "hint"
+ ],
+ "type": "object"
+}get_topic1 field changedOutput schema / properties / recoveryAdded value: +{
+ "additionalProperties": false,
+ "properties": {
+ "available": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "available_count": {
+ "maximum": 9007199254740991,
+ "minimum": -9007199254740991,
+ "type": "integer"
+ },
+ "available_truncated": {
+ "type": "boolean"
+ },
+ "did_you_mean": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "hint": {
+ "type": "string"
+ },
+ "next_tool": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "available_count",
+ "available",
+ "next_tool",
+ "hint"
+ ],
+ "type": "object"
+}answerget_entityget_latestget_overviewget_relatedget_sourcesget_topicsearchClaiming 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.
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The Laws of the Game and world football's major competitions, every claim cited to primary sources
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AI legal copilot for foreigners in Spain: immigration and housing rental, cited to Spanish law.
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Add one secure layer between your agents and this server.
Each tool has a clearly distinct role: answer/search are query entry points, get_entity/get_related handle graph traversal, get_overview/get_topic/get_latest cover different browsing modes, and get_sources handles provenance. The descriptions actively call out when to choose one tool over another, so misselection is unlikely.
The six retrieval tools consistently use the get_ prefix (get_entity, get_topic, get_sources), making the direct-fetch pattern predictable. answer and search break the prefix pattern, but they are intentional query verbs and still fit a readable action-based convention.
Eight tools is a well-scoped size for a knowledge-graph retrieval server. Each tool covers a distinct access path (overview, search, answer, entity, relations, sources, topics, freshness) without redundancy or bloat.
The tool surface covers the full read lifecycle for a corpus: orienting via overview/sources, finding content via search/topic/latest, drilling into entities, expanding via relations, and getting provenance for every claim. No obvious dead ends or missing operations for a read-only knowledge server.
