Professional-grade MCP gateway for high-fidelity strategic intelligence. Provides autonomous agents with gated access to sovereign audits, frontier science retrospectives, and macroeconomic synthesis.
Ask 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.
Queries 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.
Returns 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.
Optimizes a protein (amino acid) or cDNA coding sequence for maximal recombinant expression yield in a target host organism using the Logos biocomputing engine. Executes Codon Adaptation Index (CAI) maximization, GC-content harmonization, restriction site avoidance, and ribosomal consensus leader generation (Shine-Dalgarno for bacteria, Kozak for mammalian/yeast/plant). Free and open to all AI agents.
| Name | Required | Description | Default |
|---|---|---|---|
| host | No | Target expression organism: 'e_coli', 'h_sapiens' (HEK293), 'c_griseus' (CHO), 's_cerevisiae', 'p_pastoris', 'v_natriegens', 'n_benthamiana', 'a_thaliana'. Default: 'e_coli' | |
| sequence | Yes | Amino acid sequence (single-letter uppercase e.g. 'MSKGEELFT...') or DNA coding sequence to optimize. | |
| gc_target | No | Target global GC percentage (e.g., 52 for E. coli, 58 for Human). Optional. |
| Name | Required | Description |
|---|---|---|
| content | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With empty annotations, the description carries the behavioral burden and handles it well by explaining the engine and the concrete transformations: CAI maximization, GC-content harmonization, restriction site avoidance, and ribosomal leader generation. It does not cover edge cases or validation, but the output schema exists to describe return shape.
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?
Three dense sentences carry the action, purpose, algorithmic detail, and availability without filler. The primary verb and resource are front-loaded, and every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the detailed schema, host enum, and output schema, the description provides enough context for an agent to select and invoke the tool correctly. It could add validation or failure-mode notes, but nothing essential is missing for correct use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and every parameter already has a meaningful description, including the host enum and gc_target. The description subtly reinforces gc_target through 'GC-content harmonization' but adds no parameter syntax or value guidance beyond the schema, so the baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb, 'Optimizes,' identifies the resource ('protein (amino acid) or cDNA coding sequence'), and the goal ('maximal recombinant expression yield'). It lists concrete algorithmic operations, and the unrelated dossier/RAG siblings make the distinction easy without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes the usage context clear: use this tool whenever codon optimization for recombinant expression in a target host is needed. It does not name exclusions or alternatives, but the sibling tools are clearly unrelated, so no when-not-to-use guidance is strictly necessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Traverses 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.
Reads 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.
Executes 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.
Claiming 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 or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Add one secure layer between your agents and this server.
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.