io.github.aidi1723/omniglyph

by aidi1723

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct function: explanation, normalization, listing, validation, scanning, enforcement, lookup, and audit. The only potential overlap is the deprecated scan_code_symbols, but its description clarifies it as an alias for scan_unicode_security. All other tools have clearly separated purposes.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with underscores, such as explain_term, scan_unicode_security, lookup_glyph, and enforce_intent. The pattern is uniform across the set, with no mixing of conventions or irregular cases.
Tool Count4/5
With 17 tools, the count is slightly above the typical 3-15 range but still reasonable for the domain. Each tool serves a specific purpose, and none feel redundant. The scope of OmniGlyph justifies the number.
Completeness4/5
The tool set covers explanation, normalization, validation, scanning (security, input, output), enforcement, lookup, and auditing. While it lacks tools for creating or editing lexicon/policy packs, the focus on validation and scanning is well-covered. No critical gaps are apparent for the stated purpose.
Average 3.2/5 across 17 of 17 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 77 commits in the last 12 weeks
- Last stable release on April 25, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should fully disclose behavior, but it only states the core action. It does not mention side effects, read-only nature, error handling, or what happens to unknown tokens. The transformation process is opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (one sentence), but under-specification outweighs brevity. While it is well-structured, it sacrifices essential detail for shortness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations, output schema, and parameter descriptions, the description is far from complete. It does not cover the mode parameter, token expectations, or return value format, making it insufficient for reliable tool invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description does not explain the parameters. 'mode' (full vs compact) and 'tokens' (expected format) are left undefined, leaving the agent without necessary context to construct valid inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: normalizing glyphs and domain terms into canonical IDs. It uses a specific verb and resource, and it distinguishes from siblings like lookup_glyph and lookup_term which are lookups rather than normalization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., lookup_glyph). There is no mention of prerequisites or context that would help an agent decide between normalization and other operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description does not disclose behavioral traits such as whether this is a read-only operation, what happens on validation failure, or whether it modifies state. Annotations are absent, so the description carries full burden but fails to provide sufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it lacks essential information. However, it is front-loaded and wastes no words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter and lack of annotations or output schema, the description is insufficient. It does not explain the validation process, the meaning of 'local fact base', or the expected output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not add meaning to the 'terms' parameter beyond the schema. With 0% schema description coverage, the agent needs details on what constitutes valid terms, but none are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'validate' and identifies the resource 'output terms against the local fact base'. It distinguishes from sibling tools like validate_lexicon_pack and validate_policy_pack by focusing on output terms, but does not clarify what 'generated output terms' are.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives is provided. With many sibling validation and enforcement tools, the description should indicate appropriate contexts or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description carries the full burden. It hints at creating an audit event (a write operation) but does not explicitly state permissions, side effects, or rate limits. The behavioral impact is only implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the action and key outputs. It contains no filler but could be slightly more structured to improve readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and 4 parameters (3 required), the description lacks details on return value fields, behavior per kind enum, error conditions, and differentiation from 15 siblings. It insufficiently equips an agent for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes all parameters, with descriptions for actor_id, kind, text, and source_name. The description adds value by mentioning 'unknowns' and the audit event structure, but does not significantly enhance parameter understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns an OES explanation with an audit event including actor, sources, and unknowns. It uses a specific verb ('Return') and resource ('OES explanation', 'audit event'), but does not explicitly differentiate from siblings like explain_term or explain_glyph.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 explain_term, explain_glyph, or audit-specific tools. No exclusions, prerequisites, or context for selection are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions applying a 'strict' policy and returning evidence, but omits details about idempotency, side effects, required permissions, default behavior for missing policy, or audit logging (though actor_id hints at audit).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no extraneous words. Every part is necessary to convey purpose, action, and outcome. It is maximally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should detail the return value beyond 'allow/block decision evidence'. It does not explain what 'source-grounding' means, how policy is applied, or error/edge cases. The presence of a nested object (policy) is not elaborated. The tool's complexity is under-communicated.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 67% (policy and actor_id have descriptions). The description adds context for the main parameter 'terms' by stating they are generated output terms, but does not elaborate on policy structure beyond the schema. Baseline 3 is appropriate as the description partially compensates for missing term description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Apply strict source-grounding policy') and the resource ('generated output terms'), and specifies the outcome ('return allow/block decision evidence'). It differentiates from siblings like 'enforce_intent' and 'validate_output_terms' by focusing on source-grounding, but does not explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There are no usage conditions, prerequisites, or exclusions mentioned. The agent cannot determine when to prefer this over sibling tools like 'validate_output_terms'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description does not disclose behavioral traits like read-only nature, authentication requirements, or rate limits. For a tool that likely performs a read operation, this information is missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. However, it could be slightly expanded to include more context about the output or standard without being overly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, the description is too minimal. It does not explain what the 'OmniGlyph Explanation Standard' returns or any additional behavior, leaving the AI agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% as the parameter 'char' has a clear description in the schema. The tool description adds no additional meaning beyond the schema, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Explain' and resource 'one Unicode character', and mentions the 'OmniGlyph Explanation Standard'. However, it does not differentiate from sibling tools like 'lookup_glyph', which may cause confusion for an AI agent deciding which tool to use.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'lookup_glyph' or 'explain_term'. An AI agent would lack context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description is a single sentence that does not disclose behavioral traits. Since no annotations are provided, the description carries the full burden, but it omits critical details like whether the tool is read-only, what happens if the term is not found, or if there are authentication or rate limit considerations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently states the purpose. It is appropriately sized for a simple tool, though it could include a bit more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and no output schema, the description is minimal. It fails to provide essential context such as the nature of the operation (read vs. write), error handling, or what 'Explain' means in terms of output. For a standalone tool, this feels incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'text', which is described as 'Term text or alias to explain.' The tool description adds no additional meaning beyond the schema, so baseline 3 is appropriate. No extra constraints or format details are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('explain') and the resource ('lexical/domain term'), and references a specific standard. It distinguishes from siblings like 'explain_code_security' and 'explain_glyph' by focusing on domain terms. However, it could be more precise about what constitutes a 'lexical/domain term' and the implications of the 'OmniGlyph Explanation Standard'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With 16 sibling tools including similar 'explain' tools, the lack of usage context makes it harder for an agent to select the correct tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It mentions the 'OmniGlyph Explanation Standard' but does not explain what that entails (e.g., output format, side effects, authentication needs). The description is overly brief and does not reveal whether the tool is read-only, destructive, or has rate limits. This is insufficient for an agent to understand the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately states the tool's core function. Every word earns its place—there is no filler or repetition. It is front-loaded with the verb and resource, making it highly scannable for an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's two simple parameters and no output schema, the description is vaguely adequate but leaves gaps. It does not mention what the explanation looks like, how findings are returned, or any constraints. While not critically incomplete for a straightforward tool, an agent would benefit from knowing the output format or expected input size.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for its two parameters, so the schema already explains what 'text' and 'source_name' are. The description adds marginal value by naming the 'OmniGlyph Explanation Standard' but does not elaborate on parameter semantics beyond the schema. Thus, it meets the baseline for high coverage but does not excel.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to explain Unicode source-code security findings using a specific standard. It specifies the verb 'explain' and the resource 'Unicode source-code security findings,' distinguishing it from sibling tools like 'scan_unicode_security' (which scans) and 'explain_term' (which explains terms). However, it does not explicitly differentiate from 'explain_glyph' or 'audit_explain', so sibling differentiation is not fully realized.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 its siblings. It does not mention prerequisites, typical contexts, or when not to use it. For example, it does not clarify whether this tool should follow a scan or if it works independently. This lack of guidance forces the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description only says 'private or curated', implying restricted access, but does not disclose behaviors like read-only nature, error handling for missing terms, or side effects. The minimal description leaves significant ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence with no redundancy. It is appropriately front-loaded but could benefit from a second sentence about output or usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with one parameter and no output schema, the description omits crucial context: what the tool returns (definition, metadata?), error conditions (term not found), and whether it modifies state. This leaves the agent guessing about the response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'text', which already has a clear description. The tool's description adds 'private or curated lexical/domain term' as qualifier but it does not enhance parameter semantics beyond the schema baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'look up' and the resource 'private or curated lexical/domain term'. It distinguishes from siblings like 'explain_term' and 'normalize_tokens' by specifying the scope of terms (private/curated domain-specific).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 (e.g., explain_term) is provided. The description does not mention any prerequisites, limitations, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description does not disclose what validation entails (e.g., structural checks, content validation, side effects) or if it is read-only. A validation tool should clarify its behavior and potential impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded and concise. However, it sacrifices completeness for brevity, leaving room for more useful detail without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter, no output schema, and no annotations, the description is insufficient. It doesn't explain return values, success/failure indicators, or any additional context needed to use or interpret validation results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 'path' with 100% description coverage ('Path to a Lexicon Pack directory.'). The tool description adds no extra meaning beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'validate' and the resource 'OmniGlyph Lexicon Pack directory', specifying the key files (pack.json, terms.csv). This distinguishes it from sibling validation tools like validate_policy_pack and validate_output_terms.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Given multiple validation-related siblings, explicit usage context is missing – the description only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully convey behavioral traits. It only states the core function (scan and redact) but omits details like side effects, permissions, whether findings are stored, or what happens to the original text. This is insufficient for an agent to anticipate behavior fully.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It efficiently communicates the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and the tool's complexity (scanning with parameters), the description is too brief. It does not explain return format, error handling, or behavior when sensitive data is found, leaving significant gaps for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides descriptions for 3 of 4 parameters (75% coverage), including useful context for 'text'. The tool description itself adds no additional parameter meaning. The missing description for 'secret_terms' is not compensated by the tool description, so it meets the baseline but does not excel.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans model output for sensitive data and returns redacted text, which is a specific verb+resource+outcome. However, it does not explicitly differentiate from sibling scanning tools like scan_language_input or scan_code_symbols, which could confuse an agent about when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (before external delivery, as per the text parameter description) but provides no explicit guidance on when to or not to use this tool, nor alternatives among the many sibling tools. The context is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavior. It states 'look up' implying read-only, but does not explicitly confirm no side effects, error handling, or what happens if the glyph is not found. Insufficient detail for a tool with no annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is direct and contains no extraneous information. Every word contributes to the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with one parameter and no output schema, the description is adequate but lacks details on return value behavior (e.g., returns glyph data or null). The absence of an output schema increases the need for such description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a clear description for the single parameter 'char'. The tool description adds no additional meaning beyond the schema, which is acceptable given the parameter's simplicity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('look up'), resource ('glyph'), and scope ('single glyph in the local OmniGlyph symbol fact base'). It distinguishes from sibling tools like 'explain_glyph' and 'lookup_term' by specifying the exact resource and fact base.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Sibling tools like 'explain_glyph' and 'lookup_term' exist, but the description does not clarify when to choose this one. Missing context on prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavior. It only says 'validate' without specifying what validation entails (e.g., checks performed, error types, side effects).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence with no unnecessary words, conveying essential information efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite low complexity, the description lacks details about return values, error handling, or validation scope. No output schema or annotations compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with one parameter. Description adds context by mentioning the expected files (policy.json, intents.csv) beyond the schema's 'path to a Policy Pack directory'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates an OmniGlyph Policy Pack directory and lists expected files (policy.json and intents.csv), distinguishing it from sibling tools like validate_lexicon_pack.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, no prerequisites or exclusions provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. Discloses that tool returns a decision (allow/review/block) without executing commands. However, it lacks details on error conditions, authentication requirements, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with 14 words, front-loaded verb, no filler. Efficiently communicates core action and result.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, so description must explain return value. It names the three possible outcomes adequately. However, given 5 parameters (including a nested object), the description does not elaborate on parameter usage or edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema_description_coverage is 100%, so baseline is 3. Description does not add additional meaning beyond schema; no usage hints or examples for parameters like manifest or policy_pack_path.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states action ('apply'), resource ('intent sandbox manifest'), and outcome ('return allow, review, or block without executing commands'). Distinguishes from sibling tools which are about explanation, validation, or scanning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. Among siblings, there are validation tools but no mention of selection criteria. Context about when to choose enforce_intent over others is absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It states the tool scans for specific attacks, but does not disclose return format (e.g., boolean, list of findings), side effects, authentication requirements, or rate limits. The 'scan' verb implies read-only behavior but is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is front-loaded with the tool's purpose. Efficient and contains no redundant information; every word is necessary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a simple scan tool but lacks details about output behavior (no output schema provided). For a tool with only two parameters and no nested objects, the description should ideally describe what the tool returns (findings summary, severity, etc.) to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%; both parameters ('text' and 'source_name') are documented in the input schema. The description adds no additional meaning about parameter constraints, formats, or usage beyond what the schema provides. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool scans natural-language input for prompt-injection directives and hidden Unicode attacks, using a specific verb and resource. It distinguishes from siblings like 'scan_code_symbols' and 'scan_unicode_security' by targeting natural-language input with two specific threat types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for scanning natural-language input before model ingestion, but does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'scan_unicode_security') or when not to use it. No exclusions or alternative tool references are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool scans and identifies specific risks, but does not disclose side effects (e.g., read-only), required permissions, or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence of 15 words, conveying the core purpose without any wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a simple scanning tool, but lacks details about return values (findings format) and any behavioral context like idempotency or safety, given the absence of annotations and output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear parameter descriptions; the tool description adds no extra meaning beyond what the schema already provides, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool scans source code for specific Unicode security issues (invisible controls, homoglyphs), clearly distinguishing it from sibling tools like scan_code_symbols or scan_language_input.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for Unicode security scanning, but provides no guidance on when to use versus alternatives (e.g., scan_language_input for other input types), nor when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It accurately states the output (namespaces with summaries), but does not disclose any additional behavioral traits such as performance characteristics, ordering, or error conditions. It is adequate but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that immediately conveys the tool's purpose without any unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and no output schema, the description is sufficiently complete. It clearly states the output content, and no additional information is needed for basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, and the schema provides full coverage. The description adds context by specifying what summaries are included, which goes beyond the blank schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and the resource 'loaded lexical namespaces', specifying the types of summaries included (entry, alias, pack, source). This differentiates it from sibling tools like lookup_term or explain_term that perform different actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for listing all loaded namespaces, but it provides no explicit guidance on when to use it versus alternatives, nor any exclusions or prerequisites. Usage is clear but not explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It only states the purpose and deprecation, without disclosing behavioral details like return values, side effects, or permissions. The term 'scan' implies a read operation, but no explicit safety or behavior assurances are provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences: the first stating deprecation and the second stating the purpose. No wasted words, and the deprecation info is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two fully described parameters, but the description lacks details about the output format or what constitutes 'suspicious' Unicode symbols. Since there is no output schema and the tool is deprecated, the completeness is adequate but could be improved for non-deprecated use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides descriptions for both parameters (text and source_name), achieving 100% coverage. The tool description does not add any additional semantics beyond what is already in the schema, 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it scans for suspicious Unicode symbols in source code and explicitly notes it is a deprecated alias for scan_unicode_security, providing a specific verb+resource and differentiation from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states that the tool is deprecated and names scan_unicode_security as the alternative, giving clear guidance to avoid using this tool and use the canonical tool instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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