ReliaSim
Server Details
Reliability and bottleneck simulation for manufacturing lines; run experiments, sweep buffers.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 7 of 7 tools scored.
Each tool serves a unique, clearly defined purpose: comparison, concept explanation, bottleneck analysis, structural facts, narrative, buffer tradeoff, and gain/loss experiment. There is no ambiguity between tools.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., compare_chapters, explain_concept, run_buffer_tradeoff), making the pattern predictable and easy to understand.
With 7 tools, the set is well-scoped for a simulation analysis server. Each tool covers a distinct aspect without being too few or too many.
The tool set comprehensively covers the key analysis operations for ReliaSim: comparative analysis, bottleneck identification, buffer optimization, gain/loss analysis, and conceptual education. No obvious gaps are present.
Available Tools
7 toolscompare_chaptersAInspect
Side-by-side comparison of two chapters — tracks, topology, OEE, throughput, headline bottleneck. Output is sim-derived (no interpretation drift). Use for 'how does X compare to Y?' / 'what's the difference between Constraint-Level and LEDS-Level on the same model?' / 'what changes when we add buffers?' questions. ANTI-FABRICATION: per-chapter OEE/throughput numbers are real reference values; the side-by-side delta is computed from them, not estimated. Quote VERBATIM.
| Name | Required | Description | Default |
|---|---|---|---|
| chapter_a | Yes | First chapter id (left column of the comparison). Defaults to bs1-ct. | bs1-ct |
| chapter_b | Yes | Second chapter id (right column of the comparison). Defaults to bs1-leds — same plant data as bs1-ct, but with interrupts drilled down to named failure modes; the canonical first-look comparison. | bs1-leds |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that output is 'sim-derived (no interpretation drift)' and includes an anti-fabrication note stating numbers are real and delta is computed, not estimated. This adds valuable behavioral context beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loading the purpose in the first sentence. Every sentence earns its place, including the anti-fabrication note. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description lists the output includes (tracks, topology, OEE, throughput, headline bottleneck) and addresses reliability. It is fairly complete, though a structured list of output fields would improve it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with enums described, so baseline is 3. The description adds meaning by explaining the default for chapter_b ('same plant data as bs1-ct, but with interrupts drilled down to named failure modes; canonical first-look comparison'), which gives informative context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool does 'side-by-side comparison of two chapters' listing specific metrics (tracks, topology, OEE, throughput, headline bottleneck). This distinguishes it from siblings like explain_concept, find_bottleneck, or get_chapter_facts, which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides example questions ('how does X compare to Y?', etc.) that illustrate when to use the tool. It implies usage for comparative queries but does not explicitly state when not to use or mention alternatives, though the sibling list provides context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
explain_conceptAInspect
Definitional primer for ReliaSim's framework concepts — Constraint, Buffer, Interrupt, Converter, cascading losses, OEE, Gain/Loss methodology, Buffer Tradeoff. Returns bundled theory content, NOT interpretation of any specific simulation run. Use for 'what is X?' / 'how does X work?' / 'explain the framework' questions. For line-specific claims (throughput, availability, what-if), call the sim tools instead.
| Name | Required | Description | Default |
|---|---|---|---|
| concept | Yes | Which concept to explain. Returns a definitional primer — theory, not interpretation of a specific simulation run. Use for 'what is a Constraint?' / 'what are cascading losses?' / 'explain Gain-Loss'. | constraint |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist; the description carries the burden. It states it returns 'bundled theory content, NOT interpretation' which implies read-only behavior. However, it could explicitly state no side effects or auth requirements, but for a knowledge tool this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, each providing essential information. Front-loaded with purpose, then usage guidance. No redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains return type (theory, not interpretation) and scope. It is complete for a simple explanatory tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a detailed description for 'concept'. The tool description does not add significant meaning beyond the schema; it repeats similar information. Thus baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a 'Definitional primer' for ReliaSim framework concepts, lists specific concepts, and distinguishes from siblings by explicitly noting it does not interpret simulation runs, which aligns with sibling tools that perform simulation analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit usage guidance: 'Use for 'what is X?' / 'how does X work?' / 'explain the framework' questions.' and 'For line-specific claims... call the sim tools instead.' This provides clear when-to-use and when-not-to-use with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
find_bottleneckAInspect
Single Run bottleneck analysis for the selected chapter — which node has the worst availability, per-interrupt downtime split, throughput, OEE. All eight chapters return verified dys-cli sales-prototype numbers. ANTI-FABRICATION: numbers in the response are canonical reference values from real dys-cli engine runs. Quote them VERBATIM. Do not round, estimate, or recall from training data. For follow-ups about the same chapter, re-call this tool.
| Name | Required | Description | Default |
|---|---|---|---|
| chapter | No | Which curriculum chapter the tool should answer about. Format: `bs<1-5>-<ct|leds>`. Both tracks run on the same real plant data — `ct` = Constraint-Level (interrupts rolled up to one Weibull per machine, 5 total) and `leds` = LEDS-Level (interrupts drilled down to named failure modes, 36 total). Defaults to bs1-ct when omitted. | bs1-ct |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It warns against fabrication, instructs to quote numbers verbatim, and implies data consistency by re-call instruction. This discloses important behavioral traits, though it could mention read-only or non-destructive nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: three short sentences and an anti-fabrication note. Every sentence adds value: purpose, data verification, and behavioral instruction. It is front-loaded and avoids redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one enum parameter and no output schema, the description adequately covers purpose, output metrics, and data authenticity. It lacks explicit output structure but lists the key metrics returned, making it sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the schema already fully describes the 'chapter' parameter, including enum values and their meaning. The description adds no additional parameter semantics beyond 'selected chapter,' so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: performing bottleneck analysis for a selected chapter, identifying the worst node in terms of availability, downtime split, throughput, and OEE. It distinguishes itself from siblings (e.g., compare_chapters, explain_concept) by focusing on single-run analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use the tool (for a single chapter analysis) and advises re-calling it for follow-ups on the same chapter. However, it does not explicitly state when not to use it or provide alternatives beyond mentioning sibling tools in context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_chapter_factsAInspect
Structural facts of the selected chapter — topology, rate limits, interrupt distributions, expected efficiency. Use when the user asks about the line's configuration. ANTI-FABRICATION: rates and distributions are verified .aidos-file values. Quote VERBATIM; do not estimate or substitute training-data recall.
| Name | Required | Description | Default |
|---|---|---|---|
| chapter | No | Which curriculum chapter the tool should answer about. Format: `bs<1-5>-<ct|leds>`. Both tracks run on the same real plant data — `ct` = Constraint-Level (interrupts rolled up to one Weibull per machine, 5 total) and `leds` = LEDS-Level (interrupts drilled down to named failure modes, 36 total). Defaults to bs1-ct when omitted. | bs1-ct |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It states the tool returns structural facts and includes an anti-fabrication rule. It also clarifies that rates and distributions are verified .aidos-file values, adding transparency. It does not describe side effects, which is acceptable for a read-only tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two purposeful sentences plus an essential anti-fabrication instruction. Every part adds value, and the key purpose is front-loaded. No redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no annotations, the description covers the purpose, parameter format, and usage rule adequately. It could optionally mention the return structure, but the lack of output schema makes the current description reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining the enum format ('bs<1-5>-<ct|leds>'), the meaning of each track (ct vs. leds), and the default. This goes beyond the schema's brief description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'structural facts of the selected chapter — topology, rate limits, interrupt distributions, expected efficiency'. It specifies the verb 'get' and the resource, and distinguishes from siblings like 'get_chapter_narrative' or 'compare_chapters' by emphasizing configuration facts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use when the user asks about the line's configuration', providing direct context for when to invoke. It also includes an anti-fabrication instruction for proper usage. However, it does not explicitly state when not to use the tool or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_chapter_narrativeAInspect
Long-form narrative for the selected chapter — what the chapter adds to the complexity ladder and the key teaching point. Use when the user asks 'walk me through this' or wants the conceptual primer. Pure prose, no numerical claims; safe to summarize.
| Name | Required | Description | Default |
|---|---|---|---|
| chapter | No | Which curriculum chapter the tool should answer about. Format: `bs<1-5>-<ct|leds>`. Both tracks run on the same real plant data — `ct` = Constraint-Level (interrupts rolled up to one Weibull per machine, 5 total) and `leds` = LEDS-Level (interrupts drilled down to named failure modes, 36 total). Defaults to bs1-ct when omitted. | bs1-ct |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the output is prose, safe to summarize, and contains no numerical claims. With no annotations, this adequately covers the tool's read-only, non-destructive nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no wasted words: purpose, usage, and behavioral caveats clearly front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, usage, and output type for a simple tool with one parameter and no output schema. Could mention if narrative is static or generated, but not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the description does not add extra parameter detail beyond implying 'selected chapter'. Baseline score applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it returns 'Long-form narrative for the selected chapter' and clarifies it is 'Pure prose, no numerical claims', distinguishing it from sibling tools like get_chapter_facts which likely return factual data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: 'Use when the user asks "walk me through this" or wants the conceptual primer.' Does not explicitly name alternatives but provides clear context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_buffer_tradeoffAInspect
Buffer Tradeoff experiment — sweep a buffer's capacity from 50 → 10,000 units, measure throughput gain. Shows the diminishing-returns elbow for buffer sizing. Only defined on bs4-ct and bs4-leds; each chapter has THREE inline buffers with different placements (pass buffer id to pick one). Compare CT vs LEDS on the same slot to see why interrupt-detail level changes buffer ROI math (e.g. b3: CT +23.7% vs LEDS +64.2%). Use when the user asks 'how big should the buffer be?' / 'do buffers help on this line?' / 'which buffer position gives the most gain?' / 'what's the diminishing-returns point?'. ANTI-FABRICATION (CRITICAL): the specific tradeoff numbers (e.g. CT +23.7% vs LEDS +64.2%) are sweep-derived reference values. Quote VERBATIM in your reply; do NOT recall similar percentages from training data — every buffer position has different math.
| Name | Required | Description | Default |
|---|---|---|---|
| buffer | No | Buffer id to sweep. The Buffer-Options Constraint-Level model has `b3` (Buffer 1, between Capper↔Labeler), `b4` (Buffer 2, between Labeler↔Case Packer), `b5` (Buffer 3, between Case Packer↔Palletizer). The Buffer-Options LEDS model has `b2` (Buffer Option 1, earliest), `b3` (Buffer Option 2, middle), `b4` (Buffer Option 3, last). Defaults to b3 if omitted — but pick the buffer that matches the question (e.g. 'the first inline buffer' = b3 on CT, b2 on LEDS). | b3 |
| chapter | No | Chapter id. Only `bs4-ct` and `bs4-leds` have buffer tradeoffs defined. | bs4-ct |
Tool Definition Quality
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 explains the behavior as an experiment that measures throughput gain and shows diminishing returns. It notes restrictions to specific chapters and buffer placement details. It does not disclose side effects or safety, but the read-only nature is implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative and front-loaded with the purpose. However, it includes a lengthy anti-fabrication note that could be more concise. Every sentence adds value, but overall slightly verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two parameters with full schema descriptions and no output schema, the description covers what the tool does, when to use it, and constraints. It lacks explicit mention of the output format or units of throughput gain, but the purpose is clear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the description adds significant context: it explains the meaning of buffer IDs for both CT and LEDS models, and clarifies the chapter restriction. This goes beyond the schema's default and enum descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool sweeps buffer capacity from 50 to 10,000 units and measures throughput gain, showing the diminishing-returns elbow. It specifies the tool is only defined on bs4-ct and bs4-leds, and each chapter has three inline buffers, distinguishing it from siblings like compare_chapters or run_gain_loss.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases: when the user asks about buffer size, help, position, or diminishing returns. It includes anti-fabrication instructions. However, it does not mention when not to use this tool or suggest alternative tools, lacking exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_gain_lossAInspect
Gain/Loss experiment — disable each interrupt one at a time, measure production recovered. Reveals the ACTUAL impact of each failure mode (Gain ≠ Loss: removing one lets others fire more often). Available on bs1-leds, bs3-leds, bs4-ct, bs4-leds. Use when the user asks 'what if we fixed X?' / 'which interrupt matters most if we actually fixed it?' / 'show me the Pareto'. ANTI-FABRICATION: per-interrupt recovered-production numbers come from real dys-cli runs. Quote VERBATIM; the Gain ≠ Loss interaction is exactly the kind of figure LLMs are prone to fabricate — don't.
| Name | Required | Description | Default |
|---|---|---|---|
| chapter | No | Which curriculum chapter the tool should answer about. Format: `bs<1-5>-<ct|leds>`. Both tracks run on the same real plant data — `ct` = Constraint-Level (interrupts rolled up to one Weibull per machine, 5 total) and `leds` = LEDS-Level (interrupts drilled down to named failure modes, 36 total). Defaults to bs1-ct when omitted. | bs1-ct |
Tool Definition Quality
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 the Gain vs Loss interaction effect and includes an anti-fabrication warning about quoting verbatim. However, it does not specify whether the tool is read-only, destructive, or any rate limits, leaving some transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with the purpose stated first, followed by usage guidance and a critical anti-fabrication note. Each sentence serves a purpose, though the chapter availability statement could be integrated more smoothly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema, the description is self-contained for a single-parameter tool. It explains the experiment's behavior, the interaction effect, and provides usage examples. The anti-fabrication warning adds essential context for safe deployment. It feels complete and actionable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter 'chapter', and the schema already explains the format and levels. The description adds value by restricting available chapters to a subset (bs1-leds, etc.), which is crucial for correct invocation. This goes beyond the schema's description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: running a Gain/Loss experiment by disabling interrupts one at a time to measure production recovered, with a focus on the Gain vs Loss distinction. It also provides specific usage contexts ('what if we fixed X?', 'which interrupt matters most?') that differentiate it from siblings like 'find_bottleneck' or 'explain_concept'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use scenarios (e.g., user asks about fixing failures or Pareto) and mentions available chapters. However, there is a mismatch: the description limits availability to 'bs1-leds, bs3-leds, bs4-ct, bs4-leds', while the schema's enum includes additional values like 'bs1-ct', which could confuse an agent.
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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