Read-only AI project discovery, verification, comparison, shortlisting, and stack planning.
Browse rankings, collections, categories, scenarios, topics or filtered projects.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | ||
| layer | No | ||
| limit | No | ||
| query | No | ||
| topic | No | ||
| locale | No | en | |
| offset | No | ||
| license | No | ||
| ranking | No | ||
| category | No | ||
| scenario | No | ||
| use_case | No | ||
| collection | No | ||
| deployment | No | ||
| request_id | No | ||
| radar_topic | No | ||
| github_topic | No | ||
| resource_type | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds context about the facets being browsable (rankings, collections, categories, etc.) but does not disclose matching logic, pagination behavior, or result-level details. It is consistent with the annotations and does not contradict them, but also adds only modest behavioral context beyond 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 and front-loads the verb, but the brevity crosses into under-specification. The list of browse facets is useful, yet the structure leaves no room for usage guidance or parameter clarification, so the conciseness is achieved at the expense of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (18 optional parameters) and the presence of several closely related siblings, the description is too thin. It does not explain how 'filtered projects' relate to the listed facets, what the ranking/scenario/topic fields mean in practice, or when to prefer this over search_ai_projects. The output schema mitigates return-value explanation, but other contextual gaps remain.
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 carries the burden of explaining 18 parameters. It clarifies a few categories (ranking, collection, category, scenario, topic, query), but leaves many parameters ambiguous (role, layer, license, deployment, radar_topic, github_topic, resource_type, request_id, use_case). The description does not compensate for the schema's lack of parameter-level 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 names a specific verb ('Browse') and resource ('radar projects'), and enumerates the key entry points: rankings, collections, categories, scenarios, topics, or filtered projects. It is distinguishable from the sibling browse_radar_skills (skills vs projects) and search_ai_projects (browse vs search), though it does not explicitly contrast itself with them.
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 versus search_ai_projects, get_radar_overview, or browse_radar_skills. The description only implies browsing/filtering use; it states no exclusions, prerequisites, or alternative selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Browse/filter the Radar Skills library or open one Skill by ID.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | ||
| sort | No | ||
| limit | No | ||
| query | No | ||
| locale | No | en | |
| offset | No | ||
| license | No | ||
| category | No | ||
| skill_id | No | ||
| request_id | No | ||
| installable | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already carry the safety profile: readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. The description adds a useful distinction between library browsing and opening a single Skill by ID, but provides no further behavioral context such as result shape or filtering 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 a single, front-loaded sentence with no filler. Every word adds meaning, and it communicates the core behavior efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having annotations and an output schema, the tool has 11 undocumented parameters and multiple usage modes. The one-line description does not explain how parameters combine, when to use direct ID lookup versus filtering, or what distinguishes the two modes. An agent would need significant inference to call this correctly in non-trivial cases.
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%, and the tool has 11 parameters. The description only gestures at 'one Skill by ID,' which maps to skill_id, and 'browse/filter' at a high level. It leaves kind, sort, limit, query, locale, offset, license, category, request_id, and installable unexplained.
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 action and resource: 'Browse/filter the Radar Skills library or open one Skill by ID.' It clearly distinguishes the tool from sibling project-focused tools like browse_radar_projects by naming the Radar Skills library explicitly.
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 usage scenarios: browsing/filtering versus fetching a single skill by ID. However, it does not explicitly say when to prefer this tool over alternatives such as browse_radar_projects or search_ai_projects, leaving that decision to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare two to five projects in one explicit decision context.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| context | No | ||
| criteria | No | ||
| request_id | No | ||
| project_ids | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description adds little beyond the project-count constraint. It does not mention output format, errors, or behavior for invalid counts; the output schema may cover return values, but the description itself contributes minimal behavioral transparency. No contradiction exists.
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 with no filler and front-loads the core action and constraint. It is appropriately concise for a tool whose annotations and output schema carry the safety and return-value burden.
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 five parameters, zero schema descriptions, and a sibling list with overlapping comparison/search tools, this one-line description is not enough for reliable selection and invocation. The output schema reduces the need to document return values, but context/criteria semantics and usage boundaries remain 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 description coverage is 0%, so the description must compensate, but it only clarifies that project_ids should contain two to five projects. The semantics of context, criteria, locale, and request_id are left entirely unexplained, making it hard for an agent to invoke the tool with correct parameter intent.
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 ('Compare'), names the resource ('projects'), and adds a precise cardinality ('two to five') plus a decision context. This clearly distinguishes it from siblings like search_ai_projects or find_alternatives, and aligns with the annotation title 'Compare open-source AI projects'.
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 phrase 'in one explicit decision context' implies the intended use case, and the range 'two to five' sets a boundary. However, there is no explicit guidance on when to prefer this tool over siblings such as find_alternatives or browse_radar_projects, and no exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compose a candidate open-source AI stack and expose unknown compatibility.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| request_id | No | ||
| constraints | No | ||
| business_goal | Yes | ||
| existing_stack | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds 'candidate' and 'unknown compatibility', but does not explain what the tool does with constraints or existing_stack, nor what 'exposing compatibility' concretely means.
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 with no filler. However, the trailing phrase 'and expose unknown compatibility' is unclear and could have been worded more precisely without adding length.
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 five parameters, no schema descriptions, and only a vague sentence, the description does not give the agent enough to know how to construct inputs or interpret the output. It lacks guidance on required business_goal, how constraints influence the result, or what 'unknown compatibility' 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?
Schema description coverage is 0%, so the description needed to compensate by explaining business_goal, constraints, existing_stack, locale, and request_id. It does not; it only broadly indicates that an AI stack is composed. The parameter names are somewhat self-explanatory, but the description adds virtually no parameter-level meaning.
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 action ('Compose a candidate open-source AI stack'), which clearly identifies the tool's main resource and distinguishes it from the sibling browsing/comparison/search tools. However, the phrase 'expose unknown compatibility' is vague and does not clearly define what is exposed or how.
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 about when to use this tool versus alternatives, and there are no exclusions or conditions. The agent must infer from the name and context that this is for composing a stack rather than browsing, comparing, or searching.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find verified candidate alternatives while preserving constraints.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| project_id | Yes | ||
| request_id | No | ||
| constraints | No |
| Name | Required | Description |
|---|---|---|
No output 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 mild behavioral context by saying alternatives are 'verified' and that constraints are 'preserving,' but it does not disclose anything further such as result scope, ordering, or data sources. This is adequate but not rich.
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, front-loaded sentence with no filler. It communicates the core action and the key constraint-preservation behavior in eight words, which is ideal for 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?
The description is short, but annotations and an output schema carry some of the burden. However, it does not explicitly say what the alternatives are alternatives to, what 'verified' means, or when to pick this over sibling tools. It is minimally viable for an agent to infer usage, but 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?
Schema description coverage is 0%, and the description does not compensate by explaining project_id, locale, request_id, or constraints beyond the generic word 'constraints.' Nested schema documentation for ConstraintInput helps somewhat, but the tool-level description adds minimal parameter meaning and leans on the parameter names themselves.
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 action and resource: find verified candidate alternatives. The phrase 'while preserving constraints' clearly ties to the constraints parameter, and the annotation title adds that these are open-source project alternatives. It does not explicitly name a sibling tool, but the purpose is distinct enough from browse/search/compare 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 provides no guidance on when to use this tool versus siblings like search_ai_projects or compare_ai_projects. There is no mention of prerequisites, exclusions, or the intended scenario (e.g., finding alternatives to a specific project). Usage is only implied by the tool name and the required project_id parameter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get observed license evidence; the result is not legal advice.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| project_id | Yes | ||
| request_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint, covering the safety and side-effect profile. The description adds the useful caveat that the result is not legal advice, which is behavioral context beyond the annotations, but it does not add much else.
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 compact sentence and is front-loaded with the core purpose. However, it is so terse that it leaves out necessary context about parameters and tool selection, so the brevity is not fully 'appropriate' for the tool's complexity.
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 output schema and annotations carry a fair amount of context, but the description still leaves important gaps: it never connects the tool to a project, provides no parameter semantics, and does not explain how this differs from get_project_facts. An agent could call it with project_id, but may not understand the scope or limitations of the returned evidence.
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 parameters, but it does not mention project_id, locale, or request_id at all. The phrase 'license evidence' only weakly implies what project_id should be and provides no guidance on the 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 uses a specific verb and resource: 'Get observed license evidence' clearly states what the tool returns. It is reasonably clear, though it does not mention 'project' even though project_id is the key parameter, and it does not explicitly distinguish itself from sibling tools like get_project_facts.
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 alternatives such as get_project_facts or browse_radar_projects. The disclaimer about legal advice is useful but does not help an agent decide when this tool is the appropriate choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get current evidence-backed public facts for one project.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| project_id | Yes | ||
| request_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive, open-world behavior. The description adds useful context beyond those hints: facts are 'current', 'evidence-backed', and 'public', which shapes agent expectations about freshness, sourcing, and visibility without contradicting 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?
A single front-loaded sentence with no filler. Every modifier ('current', 'evidence-backed', 'public', 'one project') carries meaningful information that helps an agent invoke the tool correctly.
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 an output schema present, rich annotations, and only one required parameter, the tool is simple enough that the description plus schema covers what an agent needs to call it. Minor gaps around parameter semantics and sibling differentiation exist, but nothing essential is missing for basic correct invocation.
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, but it does not explain locale, request_id, or even explicitly map 'one project' to project_id. Only the enum/default in the schema and the parameter names give agents any hint about parameter purpose, leaving obvious gaps for locale formatting and request correlation.
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 names a specific verb ('Get'), resource ('evidence-backed public facts'), and clear scope ('for one project'), immediately separating this from broader list/search/overview siblings like browse_radar_projects and get_radar_overview. It is direct and unambiguous about what the tool returns.
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 phrase 'for one project' clearly frames when this tool is appropriate: when the agent needs focused facts about a specific project rather than a list, comparison, or overview. It does not explicitly name alternatives or exclusion cases, but the context is clear enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Discover current rankings, collections, categories, scenarios and filters.
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | en | |
| request_id | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds no extra behavioral context beyond these hints, such as potential result size or dynamic nature. Since annotations carry the burden, the description's lack of additional disclosure is acceptable but not distinguished.
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 is front-loaded with the key terms (rankings, collections, etc.). It is appropriately brief and free of fluff, though slightly sparse. It earns a strong score for efficiency without being verbose.
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 an output schema available and clear annotations, the description covers the core purpose but omits usage context and parameter details. For a simple read-only overview, the description is minimally adequate, but it lacks the guidance that would make it fully self-sufficient for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not explain the locale or request_id parameters. While locale likely controls language, request_id is ambiguous and could affect behavior. The description fails to compensate for the schema's lack of documentation, leaving the agent without meaningful parameter guidance.
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 discovers current rankings, collections, categories, scenarios, and filters, specifying the resource (radar overview) and the action (discover). While it does not explicitly name sibling tools, the content scope distinguishes it from more targeted tools like browse_radar_projects or compare_ai_projects. The purpose is specific enough for an agent to select it for an overview request.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention that for detailed project browsing one should use browse_radar_projects, or that this is a starting point for an overview. The context is purely declarative, leaving the agent to infer usage without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find and verify open-source AI projects from explicit requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| locale | No | en | |
| request_id | No | ||
| constraints | No |
| Name | Required | Description |
|---|---|---|
No output 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 'verify' behavior, but does not explain what verification entails, whether it performs external lookups, or how failures are handled. This is acceptable but minimal 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?
One front-loaded sentence with no filler and no redundant restatement of the title. The brevity is an asset, though it contributes to the lack of usage and parameter guidance.
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 4-parameter schema, the existence of sibling tools, and the output schema, this description is too sparse. It omits guidance on when to prefer search_ai_projects over browse_radar_projects, how to structure requirements, and what 'verify' concretely means. The output schema covers return values, but the tool is not fully self-describing.
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%; query, locale, and request_id are not explained in the schema except enum values. The description's 'explicit requirements' aligns with the constraints parameter, but it does not say how to use query versus constraints, what locale changes, or what request_id is for. The description does not compensate for the schema 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?
The description uses a specific verb pair, 'Find and verify,' and names the resource, 'open-source AI projects,' along with the source of requirements. It is clearer than the bare title and hints at a validation role. However, it does not explicitly distinguish itself from browse_radar_projects, so an agent may still be unsure whether to search or browse.
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 phrase 'from explicit requirements' implies this tool is for targeted searches where the user has concrete criteria. No when-not guidance or alternative tool names are given, so the description does not help an agent decide between this and browse_radar_projects or find_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.
browse_radar_projectsbrowse_radar_skillscompare_ai_projectscompose_ai_stackfind_alternativesget_license_evidenceget_project_factsget_radar_overviewsearch_ai_projectsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
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Services, case studies, 169 data and AI guides, and AI readiness scoring. Read-only, keyless.
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Add one secure layer between your agents and this server.
Each tool has a generally distinct role: browsing radar views, searching projects, getting facts, comparing, composing stacks, and finding alternatives. A couple of tools—notably browse_radar_projects and search_ai_projects—could be confused, but their descriptions clarify exploratory browsing versus requirement-driven search.
All tool names follow a consistent snake_case verb_noun pattern: browse_*, get_*, search_ai_projects, compare_ai_projects, compose_ai_stack, find_alternatives. The naming makes the action and target object immediately clear across the entire set.
Nine tools is a well-scoped size for an open-source AI intelligence and decision-support server. Each tool covers a distinct part of the workflow without feeling bloated or redundant.
The set covers the main workflow well: overview, browsing, search, project facts, license evidence, comparison, stack composition, and alternatives. Minor gaps like project tracking/history or export utilities are non-essential for this kind of intelligence/decision-support surface.
