Deterministic nested hive-mind orchestration over the A2A rails: hire N solvers, adversarial cross-r
Return the full content-addressed lineage of a completed job: plan, hires (including refused ones with reasons), contributions, reviews, settlements, rail calls (with simulated flags), and synthesis.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It clearly indicates this is a read operation (returns lineage) and enumerates the returned data, but does not mention permissions, side effects, or constraints like job completion requirement.
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 efficiently conveys the tool's purpose and output scope. It is front-loaded with the key action and then lists specifics, though it could be broken into multiple sentences for readability.
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?
While the description lists returned components, it lacks details on error handling (e.g., job not found or incomplete) and prerequisites (job must be completed). An output schema exists, reducing the need to explain return values, but context about when to expect this data is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not explain the job_id parameter beyond implying it identifies a completed job. The parameter is simple (single string, required), but the description adds no semantic detail about its format or usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the full content-addressed lineage of a completed job, listing specific components such as plan, hires, contributions, etc. This differentiates it from sibling tools like describe_agent or job_status.
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 the tool is for auditing completed jobs but does not explicitly state when to use it over siblings or when not to use it. No alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return fleet-standard capabilities, actions, invariants, and pricing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as whether the operation is read-only, performance implications, or authentication requirements. It only lists return contents, missing transparency.
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 sentence containing all relevant information, front-loaded with the action 'Return' and the specific items. No extra words, perfectly 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?
For a simple no-argument tool with an output schema, the description adequately lists all return categories. The existence of an output schema removes the need to explain return values further.
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 schema coverage is 100% trivially. With no parameters to document, the description correctly provides all necessary information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'fleet-standard capabilities, actions, invariants, and pricing', specifying the verb 'Return' and the exact resources. It is distinct from sibling tools like audit_job or solve, which focus on different operations.
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 does not provide explicit guidance on when to use this tool versus alternatives. Usage is implied as a way to obtain agent configuration info, but no when-not or alternative tools are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return job state, plan hash, and live budget conservation books.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It implies a read-only operation but does not explicitly confirm non-destructiveness or other behavioral traits.
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 gets straight to the point. It is well front-loaded with the action verb.
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 simple 1-parameter input and presence of an output schema, the description covers the basic return values adequately but misses limitations or format hints.
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 adds no details about the 'job_id' parameter beyond its existence. The parameter semantics are underspecified.
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 job state, plan hash, and budget conservation books. However, it does not differentiate from sibling tool 'audit_job' which may also involve job state.
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 vs. alternatives like 'audit_job' or 'solve'. The description lacks context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the registered solver pool: id, kind (worker|hive), price, capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It does not explicitly state that the tool is read-only or disclose potential side effects, permissions, or rate limits. However, the listing nature implies safe operation, and the output fields are mentioned, providing moderate transparency.
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, clear sentence with no wasted words. It is appropriately sized 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 absence of parameters and the presence of an output schema, the description is largely complete. It lists the key output fields, and the tool's purpose is clear. Minor improvement could be explicitly stating it is a read-only operation.
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 zero parameters, and the baseline is set at 4. The description does not need to add parameter info, but it does mention the output fields, which aligns with the output 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?
The description clearly states it lists the solver pool and specifies the fields returned (id, kind, price, capabilities). The verb 'list' and resource 'solver pool' are specific, and it distinguishes from sibling tools like 'solve' and 'describe_agent' which have 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?
The description provides no guidance on when to use this tool versus alternatives like 'describe_agent' or 'job_status'. No explicit context or exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Open and run a hive job in one call: hire solvers through the rails (covenant -> escrow -> meter), force reviewer!=author cross-review, synthesize only what survives review, settle every escrow exactly-once, and return a content-addressed audit hash plus bits-per-joule telemetry. Price: $5.00 per solve; model-backed solves have no free tier because provider cost must be covered. Sub-hires settle via escrow at each solver's list price.
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | ||
| depth | No | ||
| fee_bps | No | ||
| problem | Yes | ||
| subtasks | No | ||
| redundancy | No | ||
| request_id | No | ||
| covenant_id | No | ||
| payment_ref | No | ||
| budget_minor | Yes | ||
| accept_threshold | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It extensively discloses behavior: hiring solvers, cross-review enforcement, synthesis, escrow settlement, audit hash and telemetry return, and pricing. This is highly transparent.
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 dense paragraph that front-loads the primary action. It is reasonably concise, but the list of steps could be better structured with bullet points or separation for improved readability.
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 (11 parameters, no annotations, no output schema details in description), the description provides a solid overview of the workflow and side effects but lacks details on parameter semantics, error handling, and edge cases. It is somewhat complete but not fully.
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. It mentions several key parameters (problem, budget_minor, subtasks, redundancy, covenant_id, payment_ref) but fails to explain seed, depth, fee_bps, request_id, and accept_threshold. This partial coverage is adequate but not comprehensive.
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: 'Open and run a hive job in one call' followed by a detailed list of steps. It effectively distinguishes this tool from sibling tools like audit_job, job_status, etc., which serve auxiliary roles.
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 by describing the tool's function but does not explicitly state when to use this tool versus alternatives, nor does it provide contraindications or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recompute the audit digest from a presented lineage (never trusts the embedded hash) and report whether it matches a job this hive ran.
| Name | Required | Description | Default |
|---|---|---|---|
| audit_json | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It reveals key behaviors: always recomputes digest (never trusts hash) and compares to a job's digest. Does not mention side effects, authentication needs, or potential errors, but the core behavior is well disclosed.
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, no wasted words. Front-loads the action and key behavioral trait. Appropriate length for the tool 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?
Given one parameter, no annotations, and an output schema present, the description is mostly complete. It mentions what the tool does and hints at the return type ('report whether it matches'). Could be slightly improved by noting prerequisites (e.g., job existence) or potential edge 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 has one string parameter with 0% description coverage. Description adds that it is a 'presented lineage', giving some context beyond the parameter name. However, it does not specify format, structure, or constraints, which would be needed to fully compensate for the lack of schema descriptions.
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: recompute audit digest from a lineage and compare to a job's digest. It distinguishes from siblings by specifying the recomputation behavior and trust model ('never trusts the embedded hash').
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 guidance on when to use or not use this tool vs alternatives. The description implies usage when verifying an audit digest, but does not mention when to avoid it or if other tools (like audit_job) might be more appropriate.
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.
audit_jobdescribe_agentjob_statuslist_solverssolveverify_auditClaiming 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.
No comments yet. Be the first to start the discussion!
Trust gate for AI agents: multi-model adversarial consensus, signed and verifiable verdicts.
Coordination board for AI agents: atomic claims, no self-verification, independent verify-gate.
MCP server: solver auction across io.net / Akash / Render with signed receipts · Hive Civilization
BFT Progress Council MCP — 5-voter Byzantine council halts agentic loops when no real progress is
Add one secure layer between your agents and this server.
Each tool targets a distinct operation: auditing, describing agents, checking job status, listing solvers, solving jobs, and verifying audits. No overlap in purpose.
All tool names follow a consistent verb_noun snake_case pattern (e.g., audit_job, describe_agent). The naming is clear and predictable.
Six tools is well-scoped for an orchestrator server, covering essential operations without unnecessary bloat or missing core functionality.
The tool surface covers agent description, solver listing, job execution, status checking, auditing, and verification. A minor gap is the absence of a tool to list all jobs, but the core lifecycle is complete.