A
licenseA
qualityA
maintenanceAn MCP server that redacts PII/PHI from text before it ever reaches an LLM — self-hosted, fail-closed, and HIPAA-aware.
3
MIT
agent-context-api
Server Details
Free MCP context preflight with local redaction and a paid HTTP upgrade.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- dacode-dev/agent-context-api
- GitHub Stars
- 0
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Tool Definition Quality
A3.9/5.0
Tool DescriptionsA
Average 4.1/5 across 1 of 1 tools scored.
Server CoherenceA
Disambiguation5/5
With only one tool, there is no possibility of confusing it with another. The tool's purpose is singular and clearly defined.
Naming Consistency5/5
The single tool name 'preflight_context' follows a clear verb_noun pattern. With only one tool, naming consistency is trivially satisfied.
Tool Count2/5
A server with a single tool feels extremely thin, especially for an 'agent-context-api' which suggests a broader purpose. The scope is not well-served by one operation.
Completeness2/5
The one tool covers a narrow preflight operation (token counting, budgeting, redaction), but there are no complementary tools for managing or retrieving context, leaving obvious gaps for a context API.
Available Tools
1 toolpreflight_contextAInspect
Count tokens, apply an optional model-aware budget, and redact likely credentials locally. Inputs over 12,000 characters require the paid HTTP API.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text or code to preflight. | |
| model | No | Optional model name. | |
| redact | No | Redact likely credentials. | |
| token_budget | No | Optional hard token budget. |
Tool Definition Quality
A4.1/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses that redaction happens locally and that there is a size-dependent API requirement. However, it does not describe the output format, whether the input is modified, or what happens when the budget is exceeded, leaving gaps.
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 two sentences: the first lists the core functions, the second a key constraint. Every word earns its place, and the most important information 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 description explains the tool's main operations and a size limitation, but without an output schema, it fails to clarify what is returned. It also omits edge-case behavior (e.g., empty input, budget enforcement mechanics). For a tool of this complexity, more detail is needed to be fully complete.
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 coverage is 100%, but the description adds valuable nuance: 'model-aware budget' explains how token_budget interacts with the model parameter, and 'locally' clarifies that redaction is client-side. These additions go beyond the schema's basic field descriptions, providing meaningful context.
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 three specific functions: counting tokens, applying a model-aware budget, and redacting credentials. This is a specific verb+resource definition that matches the tool name and leaves no ambiguity about its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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 (whenever token counting, budgeting, or redaction is needed) and mentions a critical usage constraint (12,000-character threshold requires paid API). However, it does not explicitly discuss alternatives or exclusions, which would push it to a 5.
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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