Universal Poison Armor

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

With no annotations provided, the description carries the full disclosure burden and does so thoroughly. It states specific side effects: replacing injection patterns with `[REDACTED_INJECTION_ATTEMPT]`, replacing high-entropy suffixes with a specific placeholder, removing tracking images/beacons, and appending timestamped threat events to `security_audit.json`. This gives agents a clear model of what the tool modifies and what external effects it has.

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 well-structured with a one-sentence summary, a scannable list of transformations, and clearly labeled usage and behavior sections. Every sentence contributes meaningful operational information, and the most important purpose is front-loaded. No filler or redundant content is present.

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-parameter tool with an output schema and no annotations, the description is complete. It explains what the tool does, when to use it, when not to use it, what transformations it performs, and what audit side effect it has. The output schema covers return values, so the description does not need to restate 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?

The schema already describes `document_text` as 'The raw untrusted string content to sanitize' with 100% coverage, so the baseline is 3. The description adds semantic value by enumerating the kinds of inputs accepted — text document, file content, user input, or RAG retrieval chunk — and clarifying the threat context, which helps the agent decide what to pass. It does not add syntax details, but the schema is sufficient for that.

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 action and resource: 'Sanitize an incoming untrusted text document, file content, user input, or RAG retrieval chunk against AI poisoning.' It then lists concrete transformations such as stripping tracking pixels, neutralizing zero-width Unicode, redacting prompt injection phrases, and replacing adversarial suffixes. This makes the tool's purpose clear and distinct from its 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 explicit 'WHEN TO USE' and 'WHEN NOT TO USE' guidance. It names the sibling tools `scan_dataset_for_anomalies` and `verify_article_consensus` as alternatives for batch or consensus tasks, leaving no ambiguity about when to choose this tool.

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?

No annotations are provided, so the description takes full responsibility for behavioral disclosure. It discloses offline embedding computation, Isolation Forest fitting, cosine distance metrics, and the side effect of appending to security_audit.json. This is unusually transparent for a tool definition.

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 organized with clear sections and front-loaded purpose. It is longer than average but every section earns its place. Minor redundancy exists in repeatedly describing anomaly detection, but not enough to harm usability.

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 one parameter, no annotations, and an output schema (so return details are covered elsewhere), the description is complete: it gives method, use cases, exclusions, and side effects. Nothing an agent needs to decide whether to call it 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 description coverage is 100%, so the schema already fully documents the 'documents' parameter. The description adds examples (training examples, RAG items) and aligns with the schema's 'context chunks', but does not provide critical additional syntax or formatting semantics. Baseline 3 applies.

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 begins with a specific verb and resource: 'Scan a collection of documents, training examples, or retrieved RAG items for semantic anomalies and poisoned clusters.' It clearly differentiates from siblings by focusing on batch-level anomaly detection rather than single-document sanitization or web consensus verification.

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?

Explicit WHEN TO USE and WHEN NOT TO USE sections name exact conditions and alternatives (sanitize_document, verify_article_consensus). It leaves no ambiguity about appropriate invocation contexts.

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?

With no annotations provided, the description carries the full burden. It discloses algorithmic behavior (auditing trusted TLDs, computing pairwise cosine similarity, threshold >0.95) and the side effect of appending timestamped alerts to security_audit.json only when an attack is detected. This is transparent about both computation and potential writes.

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 well-structured with separate Usage Guidelines and Behavior & Side Effects sections. Each sentence earns its place: the purpose is front-loaded, and the WHEN NOT TO USE section clearly names alternatives without fluff. It is detailed but not redundant.

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 single fully documented parameter, an output schema, explicit usage boundaries, and disclosed side effects, the description is complete for an agent to select and invoke the tool correctly. It even names sibling tools for alternative cases, so no critical information 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 description coverage is 100%, so the baseline is 3. The description adds meaningful context by framing the 'articles' parameter as untrusted sources involved in possible coordinated flooding, and it clarifies the similarity threshold. However, it does not add new parameter-level details such as examples or additional constraints 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 states a specific verb and resource: 'Verify web search results or news articles to defend against Consensus Poisoning and Sybil attacks.' It also explains the distinctive mechanism (TLD audit + pairwise semantic cosine similarity) and explicitly distinguishes itself from sibling tools in the WHEN NOT TO USE section by naming sanitize_document and scan_dataset_for_anomalies.

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 explicit WHEN TO USE instructions for scenarios with 2+ sources on breaking, controversial, or factual queries, and explicit WHEN NOT TO USE with named alternatives. An agent can determine tool selection unambiguously without additional inference.

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