Professional-grade MCP gateway for high-fidelity strategic intelligence. Provides autonomous agents with gated access to sovereign audits, frontier science retrospectives, and macroeconomic synthesis.
Glama MCP Gateway
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Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.9/5 across 5 of 6 tools scored.
Each tool serves a distinct purpose: Q&A via RAG, updates, listing, graph traversal, full-text reading, and semantic search. No two tools overlap significantly in functionality.
All names use snake_case and follow a verb_noun pattern, though some have extra qualifiers (e.g., 'ask_intelligence_rag' vs 'list_dossiers'). The pattern is predictable and readable.
With 6 tools, the server is well-scoped for an intelligence retrieval system. Each tool contributes a necessary operation without superfluous clutter.
The tool surface covers core operations for a read-only intelligence repository: listing, searching, reading, asking questions, and tracking updates. Minor metadata features are missing but not critical.
ask_intelligence_ragAInspectAsk a natural-language question and receive structured intelligence context retrieved from Tresslers Group dossiers via RAG (Retrieval Augmented Generation). Returns relevant document chunks, source citations, conviction metadata, and graph neighborhood data. The calling LLM should synthesize the returned context into a coherent answer.
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Natural language question (e.g., 'What are the key geopolitical risks in sovereign AI policy?'). | |
| max_sources | No | Maximum number of source dossier chunks to retrieve (default 5, max 8). |
| Name | Required | Description |
|---|---|---|
| content | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool performs retrieval (RAG) and returns multiple data types, confirming it is a read-only operation. It does not mention side effects, rate limits, or auth needs, but given the nature of a query tool, the description is sufficiently transparent beyond what schema/annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences. The first sentence states the primary action and resource, the second lists return types, and the third provides usage guidance. No redundant or unnecessary information, and key points are front-loaded.
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 (RAG with multiple return types) and the presence of an output schema, the description adequately explains what the tool returns. It covers the retrieval method and expected outputs but could be more complete by mentioning potential limitations or error scenarios. Still, it provides sufficient context for an agent to use the tool effectively.
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?
Both parameters have schema descriptions (100% coverage) and are clear. The tool description adds no additional semantic value beyond the schema (e.g., no extra formatting or usage hints). Baseline score of 3 is appropriate since schema already documents parameters adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: asking a natural-language question and receiving structured intelligence context from Tresslers Group dossiers via RAG. It lists specific return types (document chunks, source citations, etc.) and distinguishes from siblings which are more specialized (e.g., query_knowledge_graph for graph queries, read_dossier for single dossiers).
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 for natural-language questions across dossiers and advises the LLM to synthesize returned context into an answer. However, it does not explicitly state when to use this tool versus its siblings (e.g., list_dossiers, read_dossier) or provide exclusion criteria, leaving the agent to infer appropriate contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_dossier_deltaAInspectQueries intelligence updates and conviction deltas attested since a specific UTC timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
| since_timestamp | Yes | ISO UTC timestamp (e.g., '2026-05-01T00:00:00Z'). |
| Name | Required | Description |
|---|---|---|
| content | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. It uses 'queries' indicating a read-only operation, and specifies input format. Lacks details on authentication or error handling, but adequately communicates safe behavior for a read tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with 10 words, perfectly front-loaded with the key verb 'Queries'. No wasted words; every part contributes to understanding.
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 output schema exists and only one parameter, description is adequate. It specifies the condition clearly. Lacks mention of pagination or limits, but sufficient for a straightforward delta query.
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 parameter 'since_timestamp' already described as an ISO UTC timestamp. The description reinforces the condition ('since a specific UTC timestamp') but adds no new detail beyond the schema. 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?
Description clearly states the verb 'queries' and the resource 'intelligence updates and conviction deltas' with a specific condition 'since a specific UTC timestamp'. It distinguishes from sibling tools like 'list_dossiers' or 'ask_intelligence_rag' which likely serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage when needing updates since a timestamp, but does not explicitly state when not to use it or mention alternative tools. Provides no exclusions or context for avoiding misuse.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_dossiersBInspectReturns a list of all available intelligence dossier slugs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| content | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description simply declares a read operation. It adds no details about access controls, ordering, or pagination, but the straightforward nature limits the need.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded, zero waste. Every word serves a 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?
Adequate for a simple listing tool, but could mention that results depend on user permissions or that it only returns slugs. Having an output schema reduces the need, but slightly more context would help.
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?
There are no parameters, so the baseline of 4 applies. The description adds no further param info, but none 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 clearly states it returns a list of all available intelligence dossier slugs, using specific verb and resource. However, it does not explicitly differentiate from sibling tools, which is adequate but not top-tier.
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 on when to use this tool versus alternatives like read_dossier or search_intelligence_matrix. The agent receives no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
query_knowledge_graphAInspectTraverses the multi-hop conceptual knowledge graph across entities (GraphRAG). Free preview available for direct 1-hop adjacencies. Deeper reasoning (2-3 hops) requires x402 settlement.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Graph traversal hop depth (1 for free preview, 2 to 3 for deep reasoning). | |
| entity_id | Yes | Target conceptual entity or dossier slug (e.g., 'Quantum AI', 'ThinkForge'). | |
| payment_proof | No | Standard x402 signed USDC transaction proof (0x...). Required for multi-hop graph access. |
| Name | Required | Description |
|---|---|---|
| content | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With empty annotations, the description must fully convey behavior. It discloses that deeper hops require payment and that a free preview exists, but does not cover other aspects like rate limits, permissions, or potential errors. The behavioral context is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no redundant words. First sentence states purpose, second explains monetization structure. Perfectly front-loaded and concise.
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 output schema exists (not detailed here), the description covers core functionality and pricing. It is complete enough for an agent to decide usage, though it omits potential error states (e.g., invalid payment_proof). Still, it's well-rounded.
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%, so baseline is 3. The description adds meaningful context: 'depth' of 1 is free, 2-3 requires payment_proof with x402 signature. This enriches the schema descriptions, justifying a higher score.
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 traverses a multi-hop knowledge graph (GraphRAG) with specific verb 'traverses' and resource 'knowledge graph'. It distinguishes from siblings like ask_intelligence_rag (which likely answers questions) by emphasizing multi-hop traversal.
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 defines when to use the free preview (1-hop) vs paid (2-3 hops) via x402 settlement. However, it does not mention alternatives among siblings, such as when to use ask_intelligence_rag instead for single-hop queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_dossierAInspectReads the pure text contents of a specific intelligence dossier. Requires x402 payment proof for premium intelligence. Without payment proof, returns a high-fidelity strategic excerpt and payment challenge.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The slug of the dossier to read (e.g., 'sovereign-ai-state-national-policy-2026'). | |
| payment_proof | No | Standard x402 signed USDC transaction proof (0x...). Required for full decrypted access. |
| Name | Required | Description |
|---|---|---|
| content | No | |
| isError | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description carries full burden. Discloses that payment proof grants full access; without it, returns a strategic excerpt and challenge. This is good transparency for a read-only tool, though no mention of side effects, rate limits, or auth details beyond payment.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-loading the main purpose and then critical usage condition. No fluff; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers core behavior including payment proof implications. Output schema likely details return format, so not needed here. Missing any mention of how to handle errors or limits, but overall sufficient for a focused read tool.
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%, so baseline is 3. The description adds context about payment proof behavior but does not elaborate on parameter syntax or constraints beyond what schema already provides. No additional value for slug 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?
Clearly states it reads pure text contents of a specific dossier. However, does not differentiate from siblings like get_dossier_delta or ask_intelligence_rag, which could cause confusion about which tool to use for similar tasks.
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?
Explicitly describes when payment proof is required and what happens without it. Provides context for choosing this tool for full text or partial excerpt. Could be improved by mentioning when not to use it (e.g., for filtered or delta queries).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_intelligence_matrixAInspectExecutes a semantic vector similarity search across the entire ThinkForge intelligence substrate. Returns relevant snippets and strategic conviction metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results to return (default 5, max 10). | |
| query | Yes | Semantic research question (e.g., 'What are the geopolitical vulnerabilities in green hydrogen supply chains?'). | |
| filter | No | Optional SQL-like metadata filter (e.g., 'convictionScore >= 0.85'). |
| Name | Required | Description |
|---|---|---|
| content | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations empty, so description must cover behavior. Only gives high-level purpose and outputs; no detail on side effects (none), error handling, or filtering behavior beyond schema. Could mention that it is read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that effectively communicates the core action and result. Efficient and front-loaded.
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 3 parameters fully described and output schema, the description is adequate but could better contextualize when to use this tool over siblings, and the meaning of 'conviction metadata'.
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 already provides full descriptions for all parameters (100% coverage). Description rephrases but adds no new semantic meaning beyond what's in schema.
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 it executes a semantic vector search across the entire intelligence substrate and returns relevant snippets with conviction metadata. Differentiates from siblings like ask_intelligence_rag which is more QA-focused.
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 explicit when-to-use or when-not-to-use compared to siblings. Implicitly for broad semantic search, but lacks guidance compared to alternatives like ask_intelligence_rag.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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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
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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
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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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