Sourced software, security and AI evidence, plus scoped requests for specialist agent help.
Scored across 11 tools
Tools are largely distinct, with the two vulnerability-related tools (check_security_exposure and lookup_vulnerability) differentiated by query type: package-version versus identifier. Similarly, daystruct_status and discover_daystruct_capabilities have clear but overlapping purposes, though descriptions clarify their unique roles. No significant ambiguity that would cause misselection.
Most tools follow a verb_noun pattern in snake_case (e.g., check_release_support, get_entity_evidence, search_entities). One deviation is 'daystruct_status', which is a noun phrase rather than verb_noun, and 'discover_daystruct_capabilities' is verbose but consistent. Overall style is predictable with minor exceptions.
With 11 tools, the server is well-scoped, sitting comfortably in the typical 3-15 range. Each tool serves a distinct informational purpose, and none feel redundant or superfluous. The count is appropriate for a knowledge/security/entity-focused server.
The server covers a comprehensive range of read-only queries: entity evidence, vulnerability data, release support, model selection, capability discovery, and change feeds. It addresses the core informational needs one would expect from such a server, with no obvious gaps that would cause agent failures. It is not a CRUD service, so absence of write operations is acceptable.
Resolve up to four package/API questions and an optional runtime compatibility check.
| Name | Required | Description | Default |
|---|---|---|---|
| claims | Yes | ||
| compatibility | 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 is not required to restate those. The description adds the behavioral fact that it can handle up to four questions and an optional compatibility check, but that is already implied by the schema (maxItems=4 and optional compatibility). It does not disclose how results are returned, error handling, or any limitations beyond the count, so it adds marginal value over 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 a single concise sentence that front-loads the core function and constraints. There is no filler or redundancy; every word adds value. It is appropriately sized for the tool's scope.
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 nested schema with two parameters, zero schema descriptions, and no output schema, the description is insufficient. It does not explain what 'resolve' entails, what the output looks like, what constitutes a valid 'claim', or how the compatibility check behaves. An agent cannot confidently craft a correct request or interpret the response based solely on this description.
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%, so the description must compensate by explaining parameter meanings. The description hints that 'claims' likely represents package/API questions and 'compatibility' is the runtime check, but it does not define the individual fields (package, api, runtime, runtimeVersion) or their expected formats. It provides only a high-level mapping, leaving an agent to guess the exact schema semantics.
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 purpose: 'Resolve up to four package/API questions and an optional runtime compatibility check.' It uses a specific verb ('resolve') and names the resource ('package/API questions' and 'runtime compatibility'), which distinguishes it from the sibling tools like check_release_support or lookup_vulnerability. However, it could be more explicit about what 'resolve' means (e.g., provide answers, verify compatibility) and does not explicitly contrast with any sibling, so it misses the top score.
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 no guidance on when to use this tool versus alternatives. It does not mention any scenarios, prerequisites, or exclusions, nor does it reference any sibling tools. An agent must infer from the name and generic wording that this is for software questions, but there is no explicit 'when to use' or 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a product and version, return whether that release line is supported, in security-fix-only maintenance, or past end of life, with the dates behind the answer and the cycles you could upgrade into. A line with no published end-of-life returns undetermined rather than supported.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | ||
| version | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations establish read-only, idempotent, non-destructive behavior, and the description adds clear expectations about outputs and the no-EOL case. No hidden side effects or contradictions are 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?
The description is two tight sentences with no filler, and the important edge-case behavior is clearly included. It is well-structured for quick agent parsing.
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?
Even without an output schema, the description covers the core outcome, the supporting details returned, and the ambiguous no-EOL case. This is sufficient for correct invocation in most contexts.
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 provides no parameter descriptions, and the description only repeats 'product and version' without defining accepted formats, examples, or constraints. With low schema coverage, this leaves the agent to guess what values are valid.
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?
Clearly states the tool's function: given a product and version, it returns the support lifecycle status with supporting details. This distinguishes it from sibling tools focused on vulnerability lookup or general software questions.
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 explains the tool's behavior and the undetermined edge case, so an agent can infer when it applies. It does not explicitly contrast it with sibling tools like check_security_exposure or lookup_vulnerability, leaving some selection inference to the agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a package and the version you are running, return the known vulnerabilities that affect it, worst first, each with the version that fixes it. Advisories whose affected range could not be parsed are returned under undetermined; treat undetermined as unanswered, never as safe.
| Name | Required | Description | Default |
|---|---|---|---|
| package | Yes | ||
| version | Yes | ||
| ecosystem | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the readOnly and idempotent annotations by detailing the output format: 'worst first, each with the version that fixes it' and the handling of unparsable advisories under 'undetermined'. This adds meaningful behavioral context about what the caller should expect.
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 concise (two sentences) and front-loaded with the core purpose. The extra clarification about undetermined advisories is relevant and adds value without unnecessary verbosity. There is no filler or redundant 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?
The description covers the main input and output expectations, including the order of results and the handling of edge cases (undetermined advisories). It lacks an explicit statement about the case where no vulnerabilities are found, and the ecosystem parameter remains unexplained, but overall it provides sufficient context for typical usage.
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 description explains the role of 'package' and 'version' by stating they are the inputs for the vulnerability check. However, 'ecosystem' is not mentioned or elaborated, leaving its purpose unclear. Since the schema provides no descriptions (coverage 0%), the description partially compensates but misses one parameter entirely.
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 function: 'Given a package and the version you are running, return the known vulnerabilities that affect it'. It specifies the verb 'return' and the resource (vulnerabilities for a package/version), and distinguishes itself by noting the output order ('worst first') and the inclusion of fixing versions. This is specific and unambiguous.
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 implies when to use the tool ('Given a package and the version you are running') and provides interpretive guidance ('treat undetermined as unanswered, never as safe'). It does not explicitly contrast with sibling tools like lookup_vulnerability, but the scenario is clearly defined, making it easy for an agent to decide when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the public Daystruct manifest and current capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 safety profile is covered. The description adds the 'public' qualifier and specifies the resource being read, which provides minor extra context. However, it does not disclose what happens on failure, whether authentication is required, or the format of the returned manifest, so it only meets the baseline for a read-only, non-destructive operation.
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 a single sentence that is front-loaded with the verb 'Read' and the resource. There is no superfluous information, and it is appropriate for a tool with no parameters. It achieves maximum conciseness while conveying the core purpose.
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 parameters and no output schema, the description is the sole source of information about the return value. It states what is read ('manifest' and 'current capabilities') but does not describe the structure, format, or any edges like error conditions or pagination. For a simple status tool, this is minimally sufficient but leaves ambiguity about the exact response shape, so it is not fully 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?
The tool has zero parameters, and the schema is trivially covered at 100% since it's an empty object. Per the rubric, a baseline of 4 applies when there are no parameters, and the description does not need to add parameter meaning. The description's mention of 'manifest and capabilities' hints at the conceptual output but is not parameter-specific.
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 action ('Read') and the resource ('the public Daystruct manifest and current capabilities'). It distinguishes from a generic status check by specifying 'public' and 'capabilities'. However, it does not explicitly differentiate from the sibling tool 'discover_daystruct_capabilities', which may overlap in purpose, so it loses one point.
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?
There is no guidance on when to use this tool versus alternatives like 'discover_daystruct_capabilities' or 'answer_software_questions'. The description provides no context for selecting it over siblings, leaving the agent to infer the appropriate usage scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find available Daystruct capabilities, or inspect a capability ID/handle and its result contract before requesting help. Provider availability is not proof a task will succeed.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| reference | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, open-world, and idempotent behavior, so the description does not contradict them. It adds a useful behavioral caveat that provider availability is not proof a task will succeed, going slightly beyond the annotation metadata.
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, using two direct sentences. It avoids unnecessary repetition and states the main purpose and an important caveat without verbosity.
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?
It covers the core purpose and one behavioral caveat, but it leaves gaps: what Daystruct is, what a 'result contract' contains, how query and reference interact, and what the output will look like. Given there is no output schema, a bit more context would be valuable.
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 provides no parameter descriptions, and the parameter names 'query' and 'reference' are only weakly mapped in the text. The phrase 'inspect a capability ID/handle' implies reference, but the description does not clearly explain the role of query, whether both can be used, or the expected format.
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 main action: finding available Daystruct capabilities or inspecting a capability reference and its result contract. It is not a pure tautology, though terms like 'Daystruct capabilities' and 'result contract' are somewhat jargon-heavy and it does not explicitly differentiate 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?
The description gives helpful usage context with 'before requesting help' and warns that provider availability does not guarantee success. However, it does not explicitly state when to prefer this tool over siblings like daystruct_status or get_context_contract, nor does it explain when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read a databank's identity, temporal, dimension, and context-packet contract.
| Name | Required | Description | Default |
|---|---|---|---|
| databank | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description aligns with the annotations (readOnlyHint, idempotentHint, non-destructive) and adds that the tool reads specific contract aspects. However, it provides no additional behavioral details such as side effects, errors, or output behavior beyond what the annotations already imply, so the bar for transparency is only partially met.
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 a single concise sentence that directly states the action and the subject. There is no extraneous content or unnecessary repetition.
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?
There is no output schema and no clear statement of what the returned contract contains or how it is structured. The phrase 'identity, temporal, dimension, and context-packet contract' hints at fields but does not provide enough context for an agent to know what to expect or how to handle the result.
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 has one parameter, 'databank', with no description and 0% schema description coverage. The tool description does not explain what a 'databank' is, what values are valid, or how the parameter affects the result, so the description fails to compensate for the missing schema-level parameter documentation.
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 clear verb ('Read') and names a resource ('a databank's identity, temporal, dimension, and context-packet contract'), but the phrase 'context-packet contract' is jargon-heavy and does not clearly convey what the tool actually returns. It is more specific than a tautology but still vague enough that an agent may not know what this tool does compared to sibling tools like get_entity_evidence or discover_daystruct_capabilities.
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?
There is no guidance about when to use this tool versus any sibling tool. The description does not mention alternatives, conditions, or scenarios, so an agent has no basis for selecting this tool over closely related ones.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read canonical facts, aliases, history, and provenance for an entity.
| Name | Required | Description | Default |
|---|---|---|---|
| entityId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the specific content returned (canonical facts, aliases, history, provenance) but does not disclose additional behaviors such as error handling, authentication requirements, or response structure. With annotations covering safety, this is adequate but not exceptional.
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 a single, concise sentence that front-loads the action ('Read') and specifies the exact content. There is no waste or redundancy, and it is appropriately structured for quick comprehension.
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 simplicity (one parameter, no output schema, annotations covering safety), the description is fairly complete. It lists the types of evidence returned, which gives the agent a clear expectation of the output. However, it does not specify behavior for missing entities or response format, which would be helpful but is not critical given the read-only, idempotent nature.
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 defines entityId as a required UUID with no additional description coverage (0%). The tool description does not mention entityId at all, nor does it explain how to obtain a valid entityId or what it represents. Since the schema does not compensate and the description does not either, this is a significant gap for a single-parameter tool.
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 ('Read') and identifies a clear resource ('canonical facts, aliases, history, and provenance for an entity'). This distinguishes it from siblings like read_recent_evidence_changes (focused on changes) and search_entities (search operation), making the tool's purpose unambiguous.
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 use this tool versus alternatives. It does not mention read_recent_evidence_changes or search_entities, nor any conditions that would favor one tool over another. The agent is left to infer usage from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a vulnerability identifier such as a CVE, return what is held about it: the affected package and version range, the version that fixes it, severity, and whether it is recorded as known-exploited. Use this when you have an identifier rather than a package you are running. Fields come back as claims carrying their own authority. status 'known' with authority 'exclusive' means Daystruct has evidence for the value; if you hold a different value, treat it as a conflict to resolve. status 'unknown' with authority 'non_exclusive' means Daystruct has no evidence either way -- an absence of evidence, which does not contradict or supersede anything you already know: keep what you independently know, and stay uncertain if you do not. A field returned as null means Daystruct looked and holds nothing for it; no other Daystruct tool will have it either. Exploitation status is never reported false from a partial catalog.
| Name | Required | Description | Default |
|---|---|---|---|
| cve | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent annotations, the description discloses non-obvious behavior: fields are returned as claims with authority, known/unknown status semantics are defined, null means Daystruct looked and holds nothing, and exploitation status is never reported false from a partial catalog. This is rich behavioral disclosure.
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 and then uses compact, information-dense sentences for the authority, status, and null semantics. No sentence is wasted; the length is justified by the subtle semantics it needs to convey.
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?
There is no output schema, so the description carries the burden of explaining what comes back: affected package/version, fix version, severity, known-exploited flag, plus how to interpret status, authority, nulls, and missing exploitation data. This is sufficient for an agent to know what the tool returns and what it means.
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 has no description for the cve parameter, but the description clarifies that it is a vulnerability identifier such as a CVE. Since there is only one required parameter and the identifier type is named, the description sufficiently compensates for the schema's lack of 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 opens with a specific verb and resource: given a vulnerability identifier such as a CVE, return held information about the affected package, fix version, severity, and known-exploited status. It also distinguishes the tool from package-oriented workflows by saying to use it when you have an identifier rather than a package you are running.
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: when you have an identifier rather than a package you are running. It also clarifies important boundary behavior, such as null meaning no other Daystruct tool will have the value either, which helps the agent decide whether additional lookups are worthwhile.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read the bounded canonical change feed.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnly, idempotent, and non-destructive behavior, so the description is consistent and adds little beyond the word 'bounded'. It does not explain pagination, consistency guarantees, or whether repeated reads return the same results.
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 a single short sentence with no redundant wording. However, the phrasing is dense and relies on undefined terms like 'canonical', so it is concise but not optimally clear.
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 does not explain what a change feed entry looks like, what 'evidence changes' means, how results are ordered, or whether there is any continuation mechanism. Given the tool's purpose, this leaves important usage context 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 only parameter, limit, is not explicitly described; 'bounded' hints at limiting results but does not state that limit controls the maximum number of feed items returned. The schema provides min/max/default constraints, but the description adds almost no semantic value for this parameter.
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 says 'Read the bounded canonical change feed', which identifies a read operation and the general resource, but 'bounded canonical change feed' is jargon and only indirectly connected to evidence through the tool name. It does not clearly distinguish this from sibling tools like get_entity_evidence or search_entities.
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 given about when to use this tool instead of alternatives, such as for incremental synchronization or polling recent evidence updates. The description lacks context about ordering, cursor use, or how this feed should be consumed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search normalized entities by databank, type, provider, or capability.
| Name | Required | Description | Default |
|---|---|---|---|
| lane | No | ||
| type | No | ||
| limit | No | ||
| databank | No | ||
| provider | No | ||
| modelType | No | ||
| capability | No | ||
| minContextWindow | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds no behavioral detail beyond that (e.g., pagination, rate limits, response format). With annotations present, the description doesn't contradict them and adds minimal context, so a 3 is fair.
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 a single, focused sentence with no wasted words. It clearly states the resource and primary filters. It could have included more guidance without becoming verbose, but the current structure is efficient. A 4 reflects its conciseness.
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 tool with eight optional parameters and no output schema, the description is severely incomplete. It doesn't explain what 'normalized entities' means, how the filters combine, what the response looks like, or any pagination details. The agent would have to inspect the schema and potentially experiment to use it correctly. The description fails to provide the necessary context.
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%, so the description must compensate for parameter meaning. It mentions four of eight parameters (databank, type, provider, capability) but gives no details about their formats, semantics, or interactions. The remaining parameters (lane, modelType, minContextWindow, limit) are undocumented in both schema and description. This is insufficient for a tool with eight optional 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?
The description states a clear verb+resource: 'Search normalized entities' and lists the main filter dimensions (databank, type, provider, capability). It is distinct from sibling tools like discover_daystruct_capabilities or get_entity_evidence, though it doesn't explicitly name them. A 4 is appropriate because it's specific but lacks explicit sibling differentiation.
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?
There is no guidance on when to use this tool versus alternatives. It doesn't mention any exclusion criteria, prerequisites, or conditions that would select this search over the sibling tools. The description only states what it does, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a need -- minimum context window, required capabilities, a price ceiling -- return the models that satisfy it, cheapest input price first, with the corroboration behind each record so you can judge how well-sourced it is. Prices are per million tokens. A model with no published price is returned under undetermined rather than excluded, so an unpriced model never reads as a disqualified one. This returns candidates and their evidence, not a recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| provider | No | ||
| capabilities | No | ||
| minContextWindow | No | ||
| maxInputPricePerMillionTokens | No | ||
| maxOutputPricePerMillionTokens | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only, idempotent, and non-destructive; the description adds useful runtime behavior: cheapest input price first, prices per million tokens, and unpriced models returned as undetermined rather than excluded. This clearly sets expectations for query results.
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 and uses four short, information-dense sentences. Each sentence adds a distinct point: selection criteria, ordering, units, missing-price handling, and output nature.
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 an all-optional parameter set and no output schema, the description adequately explains what is returned (satisfying models with corroborating evidence), how results are ordered, and key edge behavior (unpriced models). No critical selection or output caveat appears 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?
With 0% schema description coverage, the description compensates by explaining the main constraint dimensions (context window, capabilities, price ceiling) and the pricing unit. Provider and limit are not explicitly described, but their parameter names are self-explanatory and the overall semantics are clear.
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 states a specific verb ('return') and resource ('models that satisfy it'), and frames the tool as model selection under constraints. It also distinguishes output as candidates with evidence, not a recommendation, which is clear among model-related 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 specifies when to use it ('Given a need -- minimum context window, required capabilities, a price ceiling') and includes a negative usage cue ('not a recommendation'). It does not explicitly name sibling alternatives or provide an if/then routing rule, so it is strong but not maximally explicit.
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.
answer_software_questionscheck_release_supportcheck_security_exposuredaystruct_statusdiscover_daystruct_capabilitiesget_context_contractget_entity_evidencelookup_vulnerabilityread_recent_evidence_changessearch_entitiesselect_modelClaiming 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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