Axiom Agentic Tools

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already mark this as read-only and non-destructive. The description adds important behavioral context by stating it 'probes' the endpoint (making network requests) and that it returns a closed-vocabulary verdict plus 3-7 scored recommendations. It does not contradict annotations and supplements them with concrete probing behavior.

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 two sentences, front-loaded with the core purpose, and every clause adds value—from the probing surfaces to the recommendation dimensions and evidence citation. It is dense but not bloated, and highly scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, method, output structure, and evidence basis. With an output schema present, it doesn't need to detail return values, but it still names the verdict and recommendation categories. It fully addresses the tool's complexity and leaves no major contextual 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 input schema provides thorough descriptions for both parameters, and schema coverage is 100%. The description itself does not elaborate on parameters, but given that the schema fully documents them, the baseline score of 3 is appropriate—no additional meaning 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 uses a specific verb ('Audit') and a clear resource ('x402-gated endpoint'), and details the probing surfaces (402 envelope, agent-card.json, well-known surfaces, response headers). This clearly distinguishes it from sibling tools, none of which target endpoint auditing.

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 clearly implies when to use the tool: when you need to audit an x402-gated endpoint. It doesn't explicitly exclude alternatives or mention when not to use, but the high specificity of the target surface makes usage context obvious. No competing sibling tool serves the same function.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description does not contradict these. It adds valuable behavioral context beyond annotations: that the tool returns a 'deployable spec' (not an actual deployment), enforces distribution allocations summing to 100, and includes a curated risk list covering specific vectors. This helps the agent understand what it will and won't do.

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 sentence that front-loads the primary action and resource, then enumerates the key output components in a clear sequence. Every phrase adds useful information, with no filler or redundant repetition of the title or annotations.

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 moderate complexity (5 parameters, output schema present, safe annotations), the description covers the essential aspects: purpose, output deliverables, constraints, and risk categories. The presence of an output schema means return values need not be fully described, and the description is sufficiently complete for an agent to decide to invoke it.

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 input schema already provides 100% coverage with descriptive parameter explanations for all five fields. The description adds general context about the output (allocations summing to 100, templates, checklist) but does not elaborate on individual parameters beyond what the schema already states, so it meets the baseline for high schema coverage.

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 ('Design') and a clear resource ('non-security token strategy for an autonomous agent'), and lists concrete deliverables (spec, contract templates, checklist, cost estimate, risks). This clearly differentiates it from sibling tools such as agent-revenue-optimizer or mcp-security-scanner, which target completely different functions.

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 a clear use case: when you need a token strategy for an autonomous agent, including fair-launch defaults and risk analysis. However, it does not explicitly state when not to use this tool or name alternative tools, though the context is sufficient for an agent to infer applicability.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses additional behaviors: results are 'ranked, deadline-aware', 'Stale entries are flagged', and each result 'cites source_url, deadline, funding_range_usd, and last_verified_at'. This adds meaningful context about how the tool behaves beyond what annotations alone convey.

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 sentences long, front-loaded with the core action. The first sentence states purpose and output, the second adds a key behavior (staleness flagging), and the third clarifies target audience. Every sentence earns its place with no redundancy or 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?

The description is complete for a read-only list tool with rich annotations and an output schema. It explains the return fields, ranking/deadline-awareness, staleness flagging, and the intended use case. It does not need to explain the ranking algorithm or return structure further, as the output schema exists.

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 each parameter has a detailed description. The tool description itself does not add new parameter-specific semantics beyond referencing the overall context. Baseline of 3 is appropriate because the schema does the heavy lifting; the description adds no extra parameter meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function with a specific verb ('Returns') and a specific resource: 'a ranked, deadline-aware list of open Base ecosystem funding programs'. It distinguishes from sibling tools by explicitly focusing on Base funding programs with unique output fields, making it unmistakable what this tool does versus the unrelated siblings.

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 clear context: 'Designed for agents and builders shipping on Base who need a single call to surface the next applicable funding deadline.' This implies when to use (when seeking Base funding opportunities) but does not explicitly mention alternatives or exclusions. Since the sibling tools are unrelated, the guidance is sufficient, but lacks explicit 'when not to use' statements.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds valuable context: it is a 'live' feed, lists exact return fields, and discloses the $0.01/call cost. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences, each earning its place: identifies the tool, enumerates outputs, states use cases, and discloses cost. No redundant or filler content.

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 single-optional-parameter tool with a rich output schema and annotations, the description fully covers purpose, return content, use case, and cost. It is complete and 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% with full description of the 'blocks' parameter. The description does not add parameter-specific detail, but the schema already handles it. No extra value needed beyond the baseline.

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 a specific verb ('Returns') and resource ('Base network gas'), enumerating the exact data fields (gas price, base fee, priority fee, block history). It is inherently distinct from all sibling tools, which address unrelated domains.

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?

Provides clear context on when to use the tool ('timing transactions and estimating costs'), but does not explicitly mention when not to use it or reference alternatives. Given the uniqueness among siblings, this is adequate guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint and destructiveHint false, so the safety profile is known. The description adds value by disclosing the return format (structured risk report with severity levels and remediation) and the scope of checks. It also implies network access via 'endpoint,' aligning with openWorldHint. There is no contradiction.

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, front-loaded with the action and resource. The enumeration of vulnerability types is dense but each item adds useful scope information. 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?

For a tool with 3 params, a oneOf constraint, and an output schema, the description provides sufficient context: what it scans, what it returns, and the concrete vulnerability categories. It does not explain the oneOf constraint, but the schema covers that, and the output schema handles return structure. Minor gap: no mention of network behavior beyond 'endpoint,' but annotations 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?

Schema description coverage is 100%, with both manifestUrl and manifestJson fully described including mutual exclusivity. The description does not add parameter-level details beyond what the schema already provides, so 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 clearly states the tool scans an MCP server manifest or endpoint for security vulnerabilities, enumerating specific vulnerability types. This is a specific verb+resource+scope that fully distinguishes it from sibling tools, none of which are security scanners.

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 gives clear context on when to use the tool—when you need to scan an MCP server manifest or endpoint for vulnerabilities. It does not explicitly mention alternatives or exclusions, but given the unique purpose and absence of comparable siblings, the guidance is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already disclose readOnlyHint=true, destructiveHint=false, and idempotentHint=true, lowering the bar. The description adds useful context: it returns a step-by-step plan with cost/latency estimates and executable formats, and it restricts to 'public' tools. However, it doesn't go deeper into edge cases, such as pruning behavior or how it handles unavailable tools. It earns a solid 3 for adding some context without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first states the core action, the second details the return value and use case. It is front-loaded with the verb 'Compose' and includes no filler. 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 tool's moderate complexity (5 params, 1 required) and the presence of an output schema, the description provides a complete enough overview. It lists the key outputs (tool slug, endpoint URL, protocol, inputs, dependencies, costs, latency, executable plan) and mentions the supported protocols. It doesn't mention budget pruning defaults, but those are in the parameter descriptions. A 4 reflects that it is slightly incomplete for a planning tool but still sufficient.

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?

Input schema covers 100% of parameters with descriptions, so the baseline is 3. The tool description does not elaborate on individual parameters (goal, max_steps, budget_usd, etc.), but the schema already provides full semantics. The description adds no parameter-level detail beyond the schema, so a 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 clearly states the tool's primary function: 'Compose a directed acyclic graph of public x402, MCP, and A2A tools that achieves a stated agent goal.' It specifies the resource (tools) and the action (compose a DAG), and distinguishes it from siblings by focusing on workflow composition rather than revenue, token strategy, or other single-purpose tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: 'Useful for autonomous agents that need to pick which paid tools to chain.' This implies the tool's niche but doesn't explicitly mention when not to use it or name alternative tools. Since no exclusions are provided, it falls short of a 5 but is above a vague 3.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover safety (readOnlyHint, destructiveHint), and the description adds valuable context: live nature, active/resolved market scope, and a $0.01/call cost. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences, front-loaded with the primary action, and includes cost without unnecessary fluff or repetition.

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 one-parameter tool with output schema and good annotations, the description covers purpose, return fields, input format, and cost. Minor gaps like error handling are acceptable given the tool's simplicity.

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% for the single parameter, with a clear description and example. The description repeats the slug/keyword behavior but does not add semantic value beyond the 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 the tool fetches live prediction market odds from Polymarket, lists the key returned fields (probabilities, volume, liquidity, resolution status), and differentiates from unrelated siblings.

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 when Polymarket odds are needed and clarifies input as slug or keyword. No alternatives or exclusions are mentioned, but the sibling list shows no overlapping tools, so this is acceptable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark readOnly and openWorld, and the description enriches them by explaining that all checks run via GitHub API (external network call), the rate limit (50 calls/IP/day), and the detailed output structure (letter grade, per-category scores, checks with evidence, prioritized recommendations). It does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with zero fluff. The first sentence leads with the core purpose and packs in the scoring range, categories, and outputs. The second sentence adds operational details (API-based, no clone, free tier). Every clause carries useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description doesn't need to elaborate on return fields. It covers the essential additional operational context: the 8 categories, security source (OpenSSF Scorecard), API-only execution (no clone), and rate limit. This is enough for an agent to decide when to invoke the tool and what to expect.

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 input schema already fully covers the single 'repo' parameter with a pattern and example, achieving 100% schema description coverage. The description adds general context about GitHub repos but no parameter-specific detail, so the baseline of 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 opens with 'Comprehensive GitHub repository health audit', using a specific verb and resource. It enumerates 8 categories and output artifacts, making it unmistakable. Sibling tools are entirely different domains (revenue, token strategy, etc.), so it is clearly differentiated.

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 clear context: it audits GitHub repos, runs all checks via API, requires no clone, and offers free calls with a rate limit. It doesn't explicitly state when not to use it or name alternatives, but given the sibling contexts, the use case is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint and destructiveHint, so the description adds useful behavioral context beyond them, including the data source (DeFiLlama price data) and the cost ($0.01/call). This is valuable extra information without contradicting the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the primary verb 'Fetch', and includes essential details (wallet address, token list, network, USD values, cost) without any unnecessary wording. 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?

With only one parameter, an output schema, and read-only annotations, the description covers the core functionality, return values, data source, and cost. It is complete enough for an agent to select and invoke the tool, though it does not mention error cases or rate limits, which are not critical for this simple 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?

The input schema has 100% coverage, describing the 'address' parameter with a pattern and explanation. The description adds no additional parameter-level detail beyond what the schema already provides, so the baseline of 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 clearly states the tool's function: 'Fetch token balances for an Ethereum/Base wallet address.' It specifies the resource (wallet portfolio), the action (fetch token balances), and the specific tokens and network, making it distinct from siblings which are unrelated to wallet balance retrieval.

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 when querying token balances for an Ethereum/Base wallet address but provides no explicit guidance on when to use this tool versus alternatives or when not to use it. Since the siblings are not similar, the implied context is sufficient but lacks explicit exclusions or alternative recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.