claude-amplifier

by Sisuthros

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.5.3
Disambiguation5/5
Each tool has a clearly distinct purpose: lessons (learn, record_claim, verify_claim), decisions (decisions, link_decisions), patterns (global_patterns, promote_pattern, suggest_pattern_key), context (load, preflight), and auditing (audit_freshness, evidence_chain, promote_from_memory_md). No two tools overlap in function or intent.
Naming Consistency5/5
All tools consistently use the pattern 'amplify_verb_noun', with verbs like audit, load, learn, link, promote, record, suggest, verify. No mixing of conventions or camelCase; every name is descriptive and predictable.
Tool Count5/5
13 tools provide a comprehensive yet focused surface for knowledge management. The count covers all core workflows (recording, querying, linking, promoting, auditing) without being bloated or insufficient.
Completeness4/5
The tool set covers the full lifecycle of lessons, decisions, and patterns: creation, querying, linking, promotion, and verification. A minor gap is the lack of explicit listing or deletion tools, but these may be subsumed by existing query tools (e.g., amplify_decisions likely returns lists).
Average 3.8/5 across 13 of 13 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 20 commits in the last 12 weeks
- Last stable release on May 27, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
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.
This repository includes a glama.json configuration file.
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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full behavioral disclosure burden. It outlines core operations (track, get, search, supersede, etc.) but doesn't detail side effects, auth requirements, or data persistence. The schema provides parameter details but not behavioral traits.
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 concise sentence that front-loads the purpose. However, given the tool's complexity (18 parameters, multiple operations), the description is too brief and could benefit from a more structured format (e.g., bullet points) to improve scanability.
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 complex tool with many operations and nested objects, the description is minimal. No output schema exists, and the description doesn't explain return values or result formats. The effort required from the agent to understand behavior is higher than necessary.
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 detailed parameter descriptions. The one-line description adds no additional parameter-level semantics beyond the schema. Baseline 3 is appropriate as the schema already explains inputs well.
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 tracks and queries architectural/design decisions, using a specific verb and resource. It distinguishes the tool's domain (decisions) from siblings like amplify_evidence_chain or amplify_record_claim, though it doesn't explicitly differentiate operations.
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. The description only states the broad function without mentioning prerequisites, context, or exclusions. The schema lists operations but doesn't provide usage context.
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 carries full burden for behavioral disclosure. It only states 'manage' without detailing effects like whether adding a pattern immediately affects projects, required permissions, or side effects. The op enum in the schema hints at operations, but the description adds no 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core purpose. It is concise without unnecessary words. While it could be more informative, it avoids verbosity and is well-structured.
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 6 parameters, no output schema, and no annotations, the description is insufficient. It does not explain the two operations (get/add), the required fields, or the behavior of adding a pattern. The schema covers parameters, but the description lacks high-level 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%, so the baseline is 3. The description adds no additional meaning beyond the schema; it does not explain how parameters like 'applies_to' work or provide examples. It meets the baseline but does not enhance understanding.
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 states it manages cross-project patterns, which is a specific verb+resource. It implicitly distinguishes from sibling tools like amplify_promote_pattern (which likely promotes individual patterns) by focusing on global application. However, 'manage' is somewhat vague and does not specify the exact operations (get/add) that the schema provides.
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 gives context that this tool is for patterns applying to all or multiple projects, which implies when to use it. But it does not explicitly state when not to use it or mention alternative tools (e.g., amplify_promote_pattern). The usage is implied but not clarified with exclusions.
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 carry the full burden of behavioral disclosure. It only states that it loads context, but does not mention whether the operation is read-only, side effects, or what happens if the project doesn't exist. This leaves significant gaps for an agent.
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 with no fluff. It is front-loaded with the core action and resource, making it efficient and easy to parse.
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?
There is no output schema, yet the description does not explain what the tool returns (e.g., loaded context, a summary, or just confirmation). For a tool with 5 parameters, more context on parameter interactions or output behavior is needed for completeness.
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%, so the baseline is 3. The description does not add meaning beyond the schema; it merely restates the purpose. Details like token budget and prioritization are adequately covered in the schema, not the 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 tool loads saved context (decisions, lessons, patterns) for the current project at the start of a session. It uses a specific verb and resource, but does not explicitly differentiate from sibling tools like amplify_learn or amplify_global_patterns.
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 at the start of a session, but does not provide explicit guidance on when not to use, prerequisites, or alternatives among siblings. The context is clear but lacks exclusions.
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 the burden. It reveals that the tool surfaces commits, test runs, and confirmations, but does not explicitly state it is read-only or side-effect free.
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?
Two sentences, each essential: first states purpose, second adds behavioral context. No wasted 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 0% schema coverage and no output schema, the description should more fully explain parameters and behavior. It omits details about the required 'id' parameter and the 'kind' enum, making it incomplete.
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 coverage is 0% and the description does not explain the parameters 'id' or 'kind' at all. The description adds no meaning beyond the schema, leaving the agent without guidance on what values to provide.
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 shows the evidence chain for a stored lesson or decision, with specific verb 'show' and resource 'evidence chain'. It distinguishes from siblings by focusing on auditing the provenance of knowledge.
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 says 'Useful for auditing' which implies context, but does not explicitly state when to use this tool versus alternatives like amplify_verify_claim 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 are provided, so the description must fully disclose behavioral traits. It mentions 'remembers in future sessions' implying persistence, but lacks details on side effects, idempotency, or deduplication behavior (though pattern_key hints at it, the description does not explain). The tool's complexity (11 params) warrants more transparency.
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 wasted words. It efficiently conveys the core purpose without extraneous detail.
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 absence of an output schema and the tool's complexity (11 parameters, many optional), the description is underspecific. It does not explain what the agent should expect after recording (e.g., return value, success indication) or guide usage of optional fields like context, resolution, prevention, trigger, pattern_key.
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%, providing baseline value. The description adds a general purpose statement but does not enhance understanding of individual parameters beyond the schema's own descriptions. No additional semantics or context are provided.
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 records a lesson (mistake, success, insight) for future recall. It uses a specific verb 'record' and resource 'lesson', distinguishing it from sibling tools like amplify_record_claim or amplify_global_patterns.
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 should be used when the agent wants to save a lesson for future sessions, but it does not explicitly contrast with alternatives like amplify_record_claim or amplify_evidence_chain. No when-not-to-use or exclusion criteria are provided.
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 must fully disclose behavioral traits. It mentions prerequisites and effect on scoring, but omits what happens to the per-project pattern after promotion (e.g., is it removed?), whether the operation is reversible, or any side effects. This is incomplete for a mutation tool.
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 with no wasted words. It front-loads the purpose and then provides conditions and effect. Every sentence contributes value.
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 tool with one parameter and no output schema, the description covers purpose, prerequisite, and effect. But it lacks details on error cases, idempotency, and what happens to the original pattern. It is minimally adequate but could be more 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 description coverage is 100%, so baseline is 3. The tool description adds context about the prerequisite and effect, but does not enhance understanding of the parameter beyond what the schema already provides. No additional parameter details are given.
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 'Promote' and the resource 'pattern_key', specifying the scope change from per-project to global. It is distinct from siblings like 'amplify_global_patterns' which lists global patterns, and 'amplify_learn' which is about learning.
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 gives explicit prerequisites: 'Requires the key to exist in ≥2 projects with ≥1 confirmed lesson.' It also explains the consequence of promotion. However, it does not provide alternative tools for when to not use this tool, but the conditions are clear.
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 burden. It discloses the unverified nature and default confidence but omits details like response format, error conditions, or duplicate handling.
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?
Concise and front-loaded, conveying version, purpose, usage context, and sibling differentiation efficiently, though could be slightly more streamlined.
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 12 parameters and no output schema, the description lacks explanation for most parameters and does not describe the return value or behavior beyond recording, leaving significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 17%; the description only clarifies 'initial_confidence' as an override. Most parameters (type, context, resolution, etc.) remain unexplained, failing to compensate for the schema's gaps.
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 records an unverified claim with default confidence 0.5, distinguishing it from amplify_learn for confirmed/legacy records and mentioning promotion via amplify_verify_claim.
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?
Explicitly tells when to use this tool ('I just learned X' moments not confirmed) and when to use siblings (amplify_learn for confirmed records), providing clear guidance.
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 provided, so description carries full burden. It accurately describes promotion logic and optional override, but omits side effects, error handling, or auth requirements.
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?
Two concise sentences plus version number. Every word adds value: purpose, promotion rules, and usage context. No redundancy or fluff.
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?
Output schema not provided. Description covers when to use and promotion rules but lacks return value details, error conditions, or idempotency info. Adequate but has gaps.
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 75%, baseline is 3. The description adds context about the promote_to parameter's default behavior but does not significantly elaborate on other parameters beyond the 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?
Clearly states the tool attaches evidence to promote a lesson, with explicit promotion rules and scenarios. Differentiates from sibling tools like amplify_record_claim by focusing on verification and promotion based on evidence.
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?
Explicitly says 'Use this when tests pass, a commit lands, or the user explicitly confirms,' providing clear context. However, it does not mention when not to use or suggest alternatives.
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 the full burden. It discloses idempotency and lightweight nature, but does not mention potential errors (e.g., if decisions don't exist) or side effects. Adequate for a simple creation tool.
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: two sentences that are front-loaded with version and core action. Every word is necessary and adds value.
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?
Given no output schema and no annotations, the description effectively covers the tool's purpose, usage pattern, and behavioral hint (idempotency). Could mention that decision IDs must exist, but that is reasonably implied.
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 the schema already documents each parameter. The description adds no further meaning to the parameters beyond 'lightweight' and 'idempotent' context, which is baseline 3.
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 'Add a knowledge-graph link between two existing decisions,' providing a specific verb and resource. It distinguishes itself from sibling tools (e.g., amplify_audit_freshness, amplify_verify_claim) by specifying 'link' operations.
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 gives clear usage context with 'Lightweight: one call = one link. Idempotent.' This implies when to use (single link creation) and that it's safe to retry. It does not explicitly state when not to use, but the purpose is self-contained.
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, so description carries full burden. Discloses that it queries stored lessons/decisions and returns structured output. Implies read-only but doesn't explicitly state idempotency or side effects. Good but not exhaustive.
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?
Very concise: two sentences plus a brief call to action. Version number is front-loaded. No wasted words.
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?
Describes return fields (risk_level, matched patterns, etc.) despite no output schema. Covers all 4 parameters. Could explicitly state idempotency or read-only nature, but overall sufficient for a preflight check tool.
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%, so baseline is 3. Description adds value by noting alias for 'prompt' ('task') and clarifying 'context' parameter as optional extra info. Slightly improves understanding beyond 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?
Clear verb ('check') + resource ('stored lessons + decisions') and outcome ('Returns risk_level...'). Distinguishes from sibling tools by focusing on preflight checks before task execution.
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?
Explicitly states 'Call this BEFORE diving in when working on something that touches a familiar area.' Provides strong when-to-use guidance. Lacks explicit when-not-to-use, but context implies use for familiar areas where failure patterns exist.
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?
Without annotations, the description carries the burden of disclosing behavior. It communicates this is a read-only listing operation (no modifications) and its purpose (retroactive triage). Slight lack of side-effect detail, but adequate for a list tool.
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?
Two sentences with no waste. Front-loaded with version and core action. Every sentence adds essential context without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and lack of output schema, the description sufficiently explains what it returns (list of files newer than write), the condition for its use, and the overall purpose. Complete for a filtered-list tool.
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%, so the schema already documents all parameters. The description adds minimal extra meaning beyond what the schema provides, just implying use of OR between project and project_path. 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 tool lists memory files newer than the latest Amplifier write for a project. It identifies the resource (memory/<YYYY-MM-DD>.md files) and action (list), and distinguishes itself by focusing on freshness audits rather than other amplify operations.
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?
Explicitly provides two specific use cases: when amplify_context_load shows a stale-memory warning, or when suspecting unrecorded sessions. This gives clear guidance on when to employ the tool over its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It discloses that the tool is read-only ('Returns drafts only — never writes to SQLite') and describes the heuristics used. This gives an agent a clear understanding of side-effect-free 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, well-structured paragraph that front-loads the action and version, then provides heuristics, safety, and usage guidance. Every sentence adds value with no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter, no annotations, and no output schema, the description is remarkably complete. It covers purpose, heuristics, safety, and usage context, leaving little ambiguity for an agent.
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?
Only one parameter with 100% schema description coverage. The schema already explains the parameter well. The description restates the path pattern but does not add new semantics beyond what the schema provides, 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 action: 'Read a memory/<YYYY-MM-DD>.md file and surface DRAFT suggestions for amplify_learn / amplify_decisions follow-up calls.' It specifies the verb and resource, and distinguishes from siblings by mentioning heuristics and its role after amplify_audit_freshness.
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 explicitly states when to use: 'Use when amplify_audit_freshness flagged a stale day worth triaging.' It also implies not to use if you need writes ('never writes to SQLite'). However, it does not explicitly exclude other sibling tools or provide full alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses behavior: it returns up to 3 existing keys ranked by trigram similarity, or a new key suggestion if none clear the threshold. It also explains the consequence of not using it (frequency counter never aggregates). No contradictions.
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: two sentences that front-load the purpose and then provide usage context and outcome. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the return values adequately. All params are documented, and the tool's behavior and usage context are fully covered. Complete for a suggestion tool with three parameters.
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% and each parameter has a description. The description adds context by explaining that title and description help disambiguate, and that the tool uses them to find similar keys. This adds meaning beyond the schema, but not extensive format details.
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's purpose: to suggest an existing pattern_key or propose a new one for a lesson before recording. It specifies the verb 'suggest' and the resource 'pattern_key', and distinguishes it from siblings by noting it should be used before amplify_learn to prevent key duplication.
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?
Explicitly states when to use the tool: 'Use this before amplify_learn when you suspect the lesson is a recurring pattern.' It also explains why (prevents failure of aggregation) and what it returns (up to 3 existing keys or a new suggestion). This provides clear guidance on when and why to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![claude-amplifier MCP server](https://glama.ai/mcp/servers/Sisuthros/claude-amplifier/badges/card.svg)](https://glama.ai/mcp/servers/Sisuthros/claude-amplifier)

Score Badge

Copy to your README.md:

[![claude-amplifier MCP server](https://glama.ai/mcp/servers/Sisuthros/claude-amplifier/badges/score.svg)](https://glama.ai/mcp/servers/Sisuthros/claude-amplifier)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Sisuthros/claude-amplifier'

If you have feedback or need assistance with the MCP directory API, please join our Discord server