Paid agent tools plus free procurement discovery and validation. No private Mika state exposed.
Scored across 9 tools
Several tools overlap in purpose: code_review_python and code_signal_python both perform static Python review, while rescue_signal, recover_workflow, and rescue_workflow_pro all address failed-run recovery. Descriptions clarify differences through price tier, depth, and required inputs, but the boundaries remain fuzzy enough that an agent could misselect.
All names use snake_case, which is readable and largely consistent. However, naming conventions are mixed: some begin with an action verb (debug_, recover_, structure_, validate_) while others begin with a resource or domain noun (code_, procurement_, rescue_), and suffixes like _python and _pro introduce further irregularity.
Nine tools is a reasonable size for a suite of paid specialist services, and each tool has a plausible cost or scope justification. There is some tiered redundancy among the code-review and workflow-rescue tools, but the count is not excessive for the apparent breadth.
The surface covers Python code review, signal, and debug, workflow rescue/recovery tiers, JSON extraction, and procurement discovery/validation. Notable gaps remain: no standalone general text/config review outside the premium workflow tool, no non-Python code review, and no tool to actually submit or accept a procurement request beyond catalog browsing and preflight validation.
Paid $0.25 Base USDC. Review caller-supplied Python source against its intended behavior for high-confidence bugs, security issues, missing edge cases, and repair suggestions. Code is parsed/reviewed but never executed or retained.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| intent | Yes | ||
| language | No | python |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does disclose key behavioral traits: a concrete cost ($0.25 Base USDC) and the guarantee that code is 'parsed/reviewed but never executed or retained'. That is meaningful safety and billing context, though it omits any latency, rate limit, or failure-mode detail.
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 tight sentences with no filler; price and safety posture are front-loaded before the capability list, so the agent sees the constraints immediately.
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?
Purpose, cost, and non-execution/non-retention are covered, but with no output schema the description says nothing about the shape of the review result (findings, severities, repair suggestions format), and the language parameter behavior is unexplained.
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 conveys that 'code' is the source under review and 'intent' is the intended behavior it is checked against, but the third parameter (language, defaulting to python) is never referenced, leaving a documented 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?
States a specific verb ('Review') and resource ('caller-supplied Python source') plus an explicit scope: high-confidence bugs, security issues, missing edge cases, and repair suggestions. No sibling tool overlaps with code review, so an agent can select it unambiguously.
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?
Usage is implied through 'caller-supplied Python source against its intended behavior', which hints that the caller must provide both code and intent, but there is no explicit when-to-use, when-not-to-use, or alternative routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Paid $0.005 Base USDC. Fast static Python bug/security preflight with an optional cost-transparent /code-review continuation only for high/critical or multiple findings. Code is parsed but never executed or retained.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| intent | Yes | ||
| language | No | python |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and delivers real behavioral facts: an explicit price ($0.005 Base USDC), the cost-transparent opt-in continuation, and a data-handling guarantee ('parsed but never executed or retained'). It omits auth/payment prerequisites and failure modes, which keeps it short of a 5.
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, front-loaded sentences with the price and the core capability stated first; every clause carries information. Slightly jargon-heavy but no filler.
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 cost, safety posture, and escalation path, which is good for a paid static-analysis tool with no output schema. But with no annotations and 0% parameter documentation, the unexplained required 'intent' parameter leaves a meaningful gap for an agent trying to invoke 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 description coverage is 0% and all three parameters (code, intent, language) are undocumented in the schema. The description adds nothing about what 'intent' should contain or how 'language' interacts with the Python-specific tool, leaving the only required non-code parameter completely 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?
States a specific verb+resource+scope: 'Fast static Python bug/security preflight'. The 'preflight' framing plus the reference to a '/code-review continuation' implicitly separates it from the code_review_python sibling, though that sibling is never named outright.
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 escalation rule ('continuation only for high/critical or multiple findings') implies a preflight-then-deepen workflow, which is useful routing context. However it never explicitly says when to call this instead of code_review_python or debug_python_error, so usage is inferred rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Paid $0.01 Base USDC. Debug Python code from source plus an observed error/exception/traceback. Returns a bounded diagnosis, evidence, strongest static issue, and suggested fix. Submitted source is never executed or retained.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | ||
| error | Yes | ||
| intent | No | ||
| language | No | python |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses meaningful non-obvious traits: the call is paid ($0.01 Base USDC), submitted source is never executed or retained, and the return is bounded. It omits anything about failure modes, retries, payment failure handling, or limits, which prevents a 5.
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 tight sentences, front-loading the payment cost and then the purpose and return/behavior guarantees. Every sentence carries distinct, useful information with no waste.
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?
Absent an output schema, the description does state the shape of what comes back (bounded diagnosis, evidence, strongest static issue, suggested fix) and covers cost and privacy. The remaining gap is the undocumented 'intent' parameter and lack of guidance on error-input formatting.
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 alludes to 'source' and 'error' and says nothing about the 'intent' parameter or when a non-default 'language' is valid. Two of four parameters remain fully undocumented.
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?
States a specific verb and resource ('Debug Python code') and scopes it precisely with 'from source plus an observed error/exception/traceback', which implicitly separates it from siblings like code_review_python and code_signal_python that don't require an observed error. It does not name or contrast those siblings explicitly, so it stops short of a 5.
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 required inputs (source plus an observed error) but never states when to use this tool versus the sibling review/signal tools, nor any when-not conditions. No routing guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free public discovery. List Blinka/Little Life Moths bounded service offers plus the machine-readable procurement request schema and public submission channels. Does not accept work, take payment, or expose private Mika state.
| Name | Required | Description | Default |
|---|---|---|---|
| problem | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it does disclose a real side-effect profile: free, public, read-only discovery that does not accept work, take payment, or expose private state. It stops short of rates, caching, or what a caller gets back, but the non-mutating contract is clearly stated.
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 short sentences, front-loaded with the value proposition ('Free public discovery') followed by scope and then constraints. Nothing is padded, though the middle sentence is dense with unfamiliar terminology.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description should say what a caller receives, and it only loosely gestures at 'service offers' and a 'procurement request schema' without describing their shape. Combined with an undocumented parameter and no annotations, it is adequate but leaves real gaps.
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 single parameter 'problem' is optional and has 0% schema description coverage, so the schema provides no meaning at all. The description never mentions the parameter, so an agent cannot tell whether 'problem' is a free-text search query, a procurement problem statement, or something else.
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 and resource: 'List Blinka/Little Life Moths bounded service offers plus the machine-readable procurement request schema and public submission channels.' That is far more than a restatement of the name, though the idiom 'bounded service offers' and 'Blinka/Little Life Moths' is opaque and it never differentiates itself from the sibling validate_procurement_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?
'Free public discovery' implies the discovery use case and the closing sentence narrows what the tool is not for ('Does not accept work, take payment'), which implies work submission belongs elsewhere. However, no alternative is named and there is no explicit when-to-use condition relative to the sibling validate_procurement_request.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Paid $0.25 Base USDC. Diagnose a failed workflow, deployment, or agent run into completed state, blockers, retry traps, unknowns, and the smallest safe next action constrained to exact caller-supplied tool names, plus a deterministic continuation contract with explicit authority non-inheritance. The service never executes the continuation.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | ||
| run_log | Yes | ||
| constraints | No | ||
| available_tools | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so well: it discloses a cost ($0.25 Base USDC), guarantees the service never executes the continuation, states explicit authority non-inheritance, and promises a deterministic continuation contract. These are genuinely useful behavioral traits beyond a restatement of the name.
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, jargon-heavy run-on, and it is front-loaded with a payment notice ('Paid $0.25 Base USDC') rather than the tool's function. The substantive behavior is buried mid-sentence, hurting scannability.
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 side is reasonably complete for a tool with no output schema (it lists the artifacts produced), but for a 4-parameter tool with 0% schema coverage and no annotations, the inputs are under-described. Coverage is uneven rather than fully adequate.
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 there are 4 parameters, so the description must compensate. It only clarifies available_tools indirectly ('constrained to exact caller-supplied tool names') and leaves goal, run_log, and constraints entirely unexplained, giving the agent little guidance on what to supply.
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 (Diagnose) and resource (a failed workflow, deployment, or agent run) and enumerates the output artifacts: completed state, blockers, retry traps, unknowns, smallest safe next action, and a continuation contract. This is clear and concrete, but it never names or contrasts with the closely related siblings rescue_workflow_pro, rescue_signal, or debug_python_error, so the agent must infer the boundary itself.
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 scope 'failed workflow, deployment, or agent run' implies when the tool applies, but there is no explicit when-to-use vs when-not, and no routing against the similar rescue_* and debug_* siblings. Usage is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Paid $0.005 Base USDC. Cheap first-pass failed-run triage: evidence-backed blocker, retry trap, smallest next state change, and an optional smallest-sufficient continuation route when more help is warranted. No followup is purchased or executed automatically.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | ||
| run_log | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does disclose meaningful behavior: the $0.005 Base USDC cost and the fact that 'no followup is purchased or executed automatically,' clarifying it has no side effects beyond the report. It stops short of stating whether payment/auth is required or how the report is structured.
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 are front-loaded with cost then purpose, with no filler. Dense terms like 'retry trap' and 'smallest next state change' are slightly opaque but arguably the intended vocabulary of the domain.
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?
Because no output schema exists, the description helpfully enumerates the returned artifacts (blocker, retry trap, next state change, continuation route), which is needed. However, it omits any guidance on the two required parameters, leaving a real gap for a tool that cannot be invoked without them.
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%, so the description must compensate for 'goal' and 'run_log' but never mentions either. 'failed-run triage' weakly implies run_log, and nothing describes the goal parameter, leaving both inputs effectively undocumented across schema and description.
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 gives a specific verb+resource: 'failed-run triage' that produces a blocker, retry trap, next state change, and optional continuation route. It implies a distinction from a heavier variant via 'cheap first-pass,' but never names rescue_workflow_pro or recover_workflow, so sibling differentiation is left to inference.
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?
It establishes the usage context ('cheap first-pass,' 'when more help is warranted') which implies escalation to a heavier tool, but it never names the alternative or states explicit when-not-to-use conditions. Usage is implied rather than spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Paid $3.00 Base USDC. Premium failed-run repair packet: evidence-bound workflow recovery plus bounded Python code or text/config review, prioritized fixes, retry traps, and an exact tool-bounded next action. Artifacts are never executed.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | ||
| run_log | Yes | ||
| artifact | No | ||
| language | No | python | |
| constraints | No | ||
| artifact_kind | No | code | |
| artifact_intent | No | ||
| available_tools | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does add two meaningful facts: the operation costs $3.00 in Base USDC, and 'Artifacts are never executed' (a real safety guarantee). It still omits auth requirements, rate limits, failure behavior, and output format, but the cost and non-execution disclosures are substantive.
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?
It is a single, appropriately short entry front-loaded with the cost, but it is a dense run-on that stacks many buzzword concepts (evidence-bound, bounded, prioritized, retry traps, tool-bounded). Compact, but readability and structure suffer for the amount packed in.
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 complex 8-parameter tool with no annotations, no output schema, and zero schema description coverage, the description is under-specified. It sketches the deliverable packet but leaves parameter usage and required inputs (goal, run_log) unexplained, so an agent lacks what it needs to invoke 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% across 8 parameters, so the description must compensate and largely does not. It never mentions goal, run_log, artifact, constraints, artifact_intent, or available_tools; 'Python code or text/config review' only loosely gestures at language/artifact_kind, leaving most parameters 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 names a specific deliverable: an 'evidence-bound workflow recovery' packet with code/config review, prioritized fixes, and a next action. This clearly conveys what the tool produces for failed runs. However, differentiation from the sibling recover_workflow is only implied by the 'Premium'/'pro' framing rather than stated.
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 'failed-run repair' implies the context in which the tool applies, but there is no explicit guidance on when to pick this over the obvious alternative recover_workflow, nor any exclusions or prerequisites. The agent is left to infer the selection criteria from the 'Premium' label alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Paid $0.01 Base USDC. Extract JSON from unstructured text, prose, notes, or logs into a caller-supplied JSON Schema using only explicit source evidence. No URL fetch, private Mika state, or retained request body.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| schema | Yes | ||
| instruction | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses a cost model ('Paid $0.01 Base USDC'), input constraints (no URL fetch, no private Mika state, no retained request body), and an evidence-only extraction rule. It still omits error or failure behavior and any deterministic guarantees.
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 tight sentences that front-load the cost, then the core operation, then the constraints. Every sentence earns its place, and there is no filler or redundancy.
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?
No annotations and no output schema exist, and parameter descriptions are absent. The description covers purpose, cost, and the no-external-fetch/evidence-only constraints, but it leaves the instruction parameter undocumented and does not describe error handling or edge cases for an extraction tool with a caller-supplied nested schema.
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 maps 'unstructured text, prose, notes, or logs' to the text parameter and 'caller-supplied JSON Schema' to the schema parameter, but it entirely ignores the optional instruction parameter and does not explain schema format beyond the term 'JSON 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 uses a specific verb and resource: 'Extract JSON from unstructured text, prose, notes, or logs into a caller-supplied JSON Schema.' It clearly states the transformation and output target. It does not explicitly distinguish itself from sibling tools, but those siblings are unrelated, so the purpose remains clear.
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?
Usage is implied by naming source types and the caller-supplied schema, but there is no explicit when-to-use guidance, no comparison to alternatives, and no prerequisites. The 'No URL fetch, private Mika state, or retained request body' line excludes input types but does not route the agent to another tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free public procurement preflight. Validate a buyer-authored authority-verification request against the public request schema and return a non-binding scope-review handoff with candidate bounded services. No request body is retained and no payment occurs.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It usefully discloses that no request body is retained, no payment occurs, and the result is non-binding, but it omits validation failure behavior, authentication or rate-limit requirements, and whether the request is stored or logged beyond the retention statement.
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 sentences with the key framing ('Free public procurement preflight') front-loaded. The sentences are efficient and none is redundant, though the final privacy sentence could be considered secondary detail rather than essential invocation 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?
For a validation tool with no output schema and an undocumented nested request object, the description is incomplete. It states the return type broadly and notes privacy constraints, but it does not explain what constitutes a valid request, what errors or validation failures look like, or what 'candidate bounded services' means in practice.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for a single required nested 'request' object, so the description must compensate. It adds that the request is 'buyer-authored' and 'authority-verification' and must match a 'public request schema,' but it does not describe the object's expected fields or structure, leaving the agent unable to construct a valid request from this definition alone.
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?
States a specific verb ('Validate') and resource ('procurement request'), plus the outcome ('return a non-binding scope-review handoff with candidate bounded services'). It does not explicitly differentiate from sibling tools such as procurement_catalog, but the procurement-specific wording makes the purpose clear.
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?
Describes the tool as a 'free public procurement preflight,' implying it should be used before a procurement request proceeds. However, it gives no explicit when-to-use rules, no exclusions, and no comparison to alternatives like procurement_catalog.
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.
procurement_catalogvalidate_procurement_requestcode_signal_pythondebug_python_errorcode_review_pythonrescue_signalrescue_workflow_prorecover_workflowstructure_jsonAgent-native security, trust, reliability, data and procurement tools for AI workflows.
Paid x402 and MPP tools for agent discovery, payment safety, data, and DeFi.
Verified AI free tiers plus source-backed agent and MCP discovery monitoring.
Buyer-side trust oracle + capped sessions for x402 agents: verified_route, receipts, 17 free tools.
Add one secure layer between your agents and this server.
